From 4ea2edb36594187b212ec3aa9d160794aeae6abb Mon Sep 17 00:00:00 2001 From: Yunlong Song Date: Wed, 2 Jul 2025 17:31:24 -0700 Subject: [PATCH 01/10] fix nan bug in hover_env due to numerical instability of angular dynamics --- examples/drone/hover_env.py | 15 +- uv.lock | 3074 +++++++++++++++++++++++++++++++++++ 2 files changed, 3087 insertions(+), 2 deletions(-) create mode 100644 uv.lock diff --git a/examples/drone/hover_env.py b/examples/drone/hover_env.py index e48fadeea1..881383b43e 100644 --- a/examples/drone/hover_env.py +++ b/examples/drone/hover_env.py @@ -2,7 +2,12 @@ import math import copy import genesis as gs -from genesis.utils.geom import quat_to_xyz, transform_by_quat, inv_quat, transform_quat_by_quat +from genesis.utils.geom import ( + quat_to_xyz, + transform_by_quat, + inv_quat, + transform_quat_by_quat, +) def gs_rand_float(lower, upper, shape, device): @@ -90,6 +95,9 @@ def __init__(self, num_envs, env_cfg, obs_cfg, reward_cfg, command_cfg, show_vie # build scene self.scene.build(n_envs=num_envs) + # add the following line + self.drone.set_dofs_damping(torch.tensor([0.0, 0.0, 0.0, 1e-4, 1e-4, 1e-4])) # Set d + # prepare reward functions and multiply reward scales by dt self.reward_functions, self.episode_sums = dict(), dict() for name in self.reward_scales.keys(): @@ -146,7 +154,10 @@ def step(self, actions): self.last_rel_pos = self.commands - self.last_base_pos self.base_quat[:] = self.drone.get_quat() self.base_euler = quat_to_xyz( - transform_quat_by_quat(torch.ones_like(self.base_quat) * self.inv_base_init_quat, self.base_quat), + transform_quat_by_quat( + torch.ones_like(self.base_quat) * self.inv_base_init_quat, + self.base_quat, + ), rpy=True, degrees=True, ) diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000000..b887066b22 --- /dev/null +++ b/uv.lock @@ -0,0 +1,3074 @@ +version = 1 +revision = 2 +requires-python = ">=3.10, <3.13" +resolution-markers = [ + "python_full_version >= '3.12' and sys_platform == 'darwin'", + 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++++++++++-- examples/manipulation/grasp_train.py | 126 +++++-- 4 files changed, 807 insertions(+), 128 deletions(-) create mode 100644 examples/manipulation/behavior_cloning.py diff --git a/examples/manipulation/behavior_cloning.py b/examples/manipulation/behavior_cloning.py new file mode 100644 index 0000000000..e1f6931472 --- /dev/null +++ b/examples/manipulation/behavior_cloning.py @@ -0,0 +1,425 @@ +import os +import torch +import time +import torch.nn as nn +import torch.nn.functional as F + +import numpy as np +from collections import deque + +from torch.utils.tensorboard import SummaryWriter + + +class BehaviorCloning: + """Multi-task behavior cloning with action prediction and object pose estimation""" + + def __init__(self, env, cfg: dict, teacher: nn.Module, device: str = "cpu"): + self._env = env + self._cfg = cfg + self._device = device + self._teacher = teacher + self._num_steps_per_env = cfg["num_steps_per_env"] + + # Stereo rgb: 6 channels (3 left + 3 right) + rgb_shape = (6, env.image_height, env.image_width) + action_dim = env.num_actions + + # Multi-task policy with action and pose heads + self._policy = Policy(cfg["policy"], action_dim).to(device) + + # Initialize optimizer + self._optimizer = torch.optim.Adam(self._policy.parameters(), lr=cfg["learning_rate"]) + + # Experience buffer with pose data + self._buffer = ExperienceBuffer( + num_envs=env.num_envs, + max_size=self._cfg["buffer_size"], + img_shape=rgb_shape, + state_dim=self._cfg["policy"]["action_head"]["state_obs_dim"], + action_dim=action_dim, + device=self._device, + ) + + # Training state + self._current_iter = 0 + + def learn(self, num_learning_iterations: int, log_dir: str) -> None: + self._rewbuffer = deque(maxlen=100) + self._cur_reward_sum = torch.zeros(self._env.num_envs, dtype=torch.float, device=self._device) + self._buffer.clear() + + tf_writer = SummaryWriter(log_dir) + + for it in range(num_learning_iterations): + # Collect experience + start_time = time.time() + self._collect_with_rl_teacher() + end_time = time.time() + forward_time = end_time - start_time + + # Training steps for both action and pose prediction + total_action_loss = 0.0 + total_pose_loss = 0.0 + num_batches = 0 + + start_time = time.time() + generator = self._buffer.get_batches(self._cfg.get("mini_batches_size", 32), self._cfg["num_epochs"]) + for batch in generator: + # Forward pass for both action and pose prediction + pred_action = self._policy(batch["rgb_obs"].float(), batch["robot_pose"].float()) + pred_left_pose, pred_right_pose = self._policy.predict_pose(batch["rgb_obs"].float()) + + # Compute action prediction loss + action_loss = F.mse_loss(pred_action, batch["actions"].float()) + + # Compute pose estimation loss (position + orientation) + pose_left_loss = self._compute_pose_loss(pred_left_pose, batch["object_poses"].float()) + pose_right_loss = self._compute_pose_loss(pred_right_pose, batch["object_poses"].float()) + pose_loss = pose_left_loss + pose_right_loss + + # Combined loss with weights + total_loss = action_loss + pose_loss + + # Backward pass + self._optimizer.zero_grad() + total_loss.backward() + self._optimizer.step() + torch.nn.utils.clip_grad_norm_(self._policy.parameters(), self._cfg["max_grad_norm"]) + + total_action_loss += action_loss + total_pose_loss += pose_loss + num_batches += 1 + + end_time = time.time() + backward_time = end_time - start_time + + # Compute average losses + avg_action_loss = total_action_loss / num_batches if num_batches > 0 else 0.0 + avg_pose_loss = total_pose_loss / num_batches if num_batches > 0 else 0.0 + + fps = (self._num_steps_per_env * self._env.num_envs) / (forward_time) + # Logging + if (it + 1) % 10 == 0: + current_lr = self._optimizer.param_groups[0]["lr"] + + tf_writer.add_scalar("loss/action_loss", avg_action_loss, it) + tf_writer.add_scalar("loss/pose_loss", avg_pose_loss, it) + tf_writer.add_scalar("loss/total_loss", avg_action_loss + avg_pose_loss, it) + tf_writer.add_scalar("lr", current_lr, it) + tf_writer.add_scalar("buffer_size", self._buffer.size, it) + tf_writer.add_scalar("speed/forward", forward_time, it) + tf_writer.add_scalar("speed/backward", backward_time, it) + tf_writer.add_scalar("speed/fps", int(fps), it) + + # + print("--------------------------------") + info_str = f" | Iteration: {it + 1:04d}\n" + info_str += f" | Action Loss: {avg_action_loss:.6f}\n" + info_str += f" | Pose Loss: {avg_pose_loss:.6f}\n" + info_str += f" | Total Loss: {avg_action_loss + avg_pose_loss:.6f}\n" + info_str += f" | Learning Rate: {current_lr:.6f}\n" + info_str += f" | Forward Time: {forward_time:.2f}s\n" + info_str += f" | Backward Time: {backward_time:.2f}s\n" + info_str += f" | FPS: {int(fps)}" + print(info_str) + + if len(self._rewbuffer) > 0: + tf_writer.add_scalar("reward/mean", np.mean(self._rewbuffer), it) + + # Save checkpoints periodically + if (it + 1) % 50 == 0: + self.save(os.path.join(log_dir, f"checkpoint_{it + 1:04d}.pt")) + + tf_writer.close() + + def _compute_pose_loss(self, pred_poses: torch.Tensor, target_poses: torch.Tensor) -> torch.Tensor: + """Compute pose loss with separate position and orientation components.""" + # Split into position and orientation + pred_pos = pred_poses[:, :3] + pred_quat = pred_poses[:, 3:7] + target_pos = target_poses[:, :3] + target_quat = target_poses[:, 3:7] + + # Position loss (MSE) + pos_loss = F.mse_loss(pred_pos, target_pos) + + # Orientation loss (quaternion distance) + # Normalize quaternions + pred_quat = F.normalize(pred_quat, p=2, dim=1) + target_quat = F.normalize(target_quat, p=2, dim=1) + + # Quaternion distance: 1 - |dot(q1, q2)| + quat_dot = torch.sum(pred_quat * target_quat, dim=1) + quat_loss = torch.mean(1.0 - torch.abs(quat_dot)) + + return pos_loss + quat_loss + + def _collect_with_rl_teacher(self) -> None: + """Collect experience from environment using stereo rgb images and object poses.""" + # Get state observation + obs, _ = self._env.get_observations() + with torch.inference_mode(): + for _ in range(self._num_steps_per_env): + # Get stereo rgb images + rgb_obs = self._env.get_stereo_rgb_images(normalize=True) + + # Get teacher action + teacher_action = self._teacher(obs).detach() + + # Get end-effector position + ee_pose = self._env.robot.ee_pose + + # Get object pose in camera frame + # object_pose_camera = self._get_object_pose_in_camera_frame() + object_pose = torch.cat( + [ + self._env.object.get_pos(), + self._env.object.get_quat(), + ], + dim=-1, + ) + + # Store in buffer + self._buffer.add(rgb_obs, ee_pose, object_pose, teacher_action) + + # Step environment with student action + student_action = self._policy(rgb_obs.float(), ee_pose.float()) + + # Simple Dagger: use student action if its difference with teacher action is less than 0.5 + action_diff = torch.norm(student_action - teacher_action, dim=-1) + condition = (action_diff < 1.0).unsqueeze(-1).expand_as(student_action) + action = torch.where(condition, student_action, teacher_action) + + next_obs, reward, done, _ = self._env.step(action) + self._cur_reward_sum += reward + + obs = next_obs + new_ids = (done > 0).nonzero(as_tuple=False) + self._rewbuffer.extend(self._cur_reward_sum[new_ids][:, 0].cpu().numpy().tolist()) + self._cur_reward_sum[new_ids] = 0 + + def save(self, path: str) -> None: + """Save model checkpoint.""" + checkpoint = { + "model_state_dict": self._policy.state_dict(), + "optimizer_state_dict": self._optimizer.state_dict(), + "current_iter": self._current_iter, + "config": self._cfg, + } + torch.save(checkpoint, path) + print(f"Model saved to {path}") + + def load(self, path: str) -> None: + """Load model checkpoint.""" + checkpoint = torch.load(path, map_location=self._device, weights_only=False) + self._policy.load_state_dict(checkpoint["model_state_dict"]) + self._optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) + self.current_iter = checkpoint["current_iter"] + print(f"Model loaded from {path}") + + def load_finetuned_model(self, path: str) -> None: + """Load a fine-tuned model checkpoint.""" + checkpoint = torch.load(path, map_location=self._device, weights_only=False) + self._policy.load_state_dict(checkpoint["model_state_dict"]) + self._optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) + self._current_iter = checkpoint["current_iter"] + print(f"Fine-tuned model loaded from {path}") + + +class ExperienceBuffer: + """Experience buffer.""" + + def __init__( + self, + num_envs: int, + max_size: int, + img_shape: tuple[int, int, int], + state_dim: int, + action_dim: int, + device: str = "cpu", + ): + self._img_shape = img_shape + self._state_dim = state_dim + self._action_dim = action_dim + self._num_envs = num_envs + self._max_size = max_size + self._device = device + self._size = 0 + self._ptr = 0 # pointer to the next free slot in the buffer + + # Initialize buffers + self._rgb_obs = torch.zeros(max_size, num_envs, *img_shape, device=device) + self._robot_pose = torch.zeros(max_size, num_envs, state_dim, device=device) + self._object_poses = torch.zeros(max_size, num_envs, 7, device=device) + self._actions = torch.zeros(max_size, num_envs, action_dim, device=device) + + def add( + self, + rgb_obs: torch.Tensor, + robot_pose: torch.Tensor, + object_poses: torch.Tensor, + actions: torch.Tensor, + ) -> None: + """Add experience to buffer.""" + ptr = self._ptr % self._max_size + self._rgb_obs[ptr].copy_(rgb_obs) + self._robot_pose[ptr].copy_(robot_pose) + self._object_poses[ptr].copy_(object_poses) + self._actions[ptr].copy_(actions) + self._ptr = self._ptr + 1 + self._size = min(self._size + 1, self._max_size) + + def get_batches(self, mini_batches_size: int, num_epochs: int): + """Generate batches for training.""" + buffer_size = self._size * self._num_envs + indices = torch.randperm(buffer_size, device=self._device) + # calculate the size of each mini-batch + num_batches = min(buffer_size // mini_batches_size, 10) + for _ in range(num_epochs): + for batch_idx in range(num_batches): + start = batch_idx * mini_batches_size + end = start + mini_batches_size + mb_indices = indices[start:end] + + # Yield a mini-batch of data + batch = { + "rgb_obs": self._rgb_obs.view(-1, *self._img_shape)[mb_indices], + "robot_pose": self._robot_pose.view(-1, self._state_dim)[mb_indices], + "object_poses": self._object_poses.view(-1, 7)[mb_indices], + "actions": self._actions.view(-1, self._action_dim)[mb_indices], + } + yield batch + + def clear(self) -> None: + """Clear the buffer.""" + self._rgb_obs.zero_() + self._robot_pose.zero_() + self._actions.zero_() + self._size = 0 + self._ptr = 0 + + def is_full(self) -> bool: + """Check if buffer is full.""" + return self._size == self._max_size + + @property + def size(self) -> int: + """Get buffer size.""" + return self._size + + +class Policy(nn.Module): + """Multi-task behavior cloning policy with shared stereo encoder/decoder.""" + + def __init__(self, config: dict, action_dim: int): + super().__init__() + + # Shared encoder for both left and right cameras + self.shared_encoder = self._build_cnn(config["vision_encoder"]) + + # Feature fusion layer to combine stereo features + vision_encoder_conv_out_channels = config["vision_encoder"]["conv_layers"][-1]["out_channels"] + vision_encoder_output_dim = vision_encoder_conv_out_channels * 4 * 4 + + self.feature_fusion = nn.Sequential( + nn.Linear(vision_encoder_output_dim * 2, vision_encoder_output_dim), # 2 cameras + nn.ReLU(), + nn.Dropout(0.1), + ) + + # MLP for action prediction + mlp_cfg = config["action_head"] + self.state_obs_dim = config["action_head"]["state_obs_dim"] + if self.state_obs_dim is not None: + mlp_cfg["input_dim"] = vision_encoder_output_dim + self.state_obs_dim + else: + mlp_cfg["input_dim"] = vision_encoder_output_dim + mlp_cfg["output_dim"] = action_dim + self.mlp = self._build_mlp(mlp_cfg) + + # MLP for pose prediction + pose_mlp_cfg = config["pose_head"] + pose_mlp_cfg["input_dim"] = vision_encoder_output_dim + pose_mlp_cfg["output_dim"] = 7 + self.pose_mlp = self._build_mlp(pose_mlp_cfg) + + # Force float32 for better performance + self.float() + + def _build_cnn(self, config: dict) -> nn.Sequential: + """Build CNN encoder for grayscale images.""" + layers = [] + + # Build layers from configuration + for conv_config in config["conv_layers"]: + layers.extend( + [ + nn.Conv2d( + conv_config["in_channels"], + conv_config["out_channels"], + kernel_size=conv_config["kernel_size"], + stride=conv_config["stride"], + padding=conv_config["padding"], + ), + nn.BatchNorm2d(conv_config["out_channels"]), + nn.ReLU(), + ] + ) + + # Add adaptive pooling if specified + if config.get("pooling") == "adaptive_avg": + layers.append(nn.AdaptiveAvgPool2d((4, 4))) + + return nn.Sequential(*layers) + + def _build_mlp(self, config: dict) -> nn.Sequential: + mlp_input_dim = config["input_dim"] + layers = [] + for hidden_dim in config["hidden_dims"]: + layers.extend([nn.Linear(mlp_input_dim, hidden_dim), nn.ReLU()]) + mlp_input_dim = hidden_dim + layers.append(nn.Linear(mlp_input_dim, config["output_dim"])) + return nn.Sequential(*layers) + + def get_features(self, rgb_obs: torch.Tensor) -> torch.Tensor: + # Split stereo rgb images + left_rgb = rgb_obs[:, 0:3] # First 3 channels (RGB) + right_rgb = rgb_obs[:, 3:6] # Last 3 channels (RGB) + + # Use shared encoder for both images + left_features = self.shared_encoder(left_rgb).flatten(start_dim=1) + right_features = self.shared_encoder(right_rgb).flatten(start_dim=1) + return left_features, right_features + + def forward(self, rgb_obs: torch.Tensor, state_obs: torch.Tensor | None = None) -> dict: + """Forward pass with shared stereo encoder for rgb images.""" + # Ensure float32 for better performance + rgb_obs = rgb_obs.float() + if state_obs is not None: + state_obs = state_obs.float() + + # Get features + left_features, right_features = self.get_features(rgb_obs) + + # Concatenate features (much more efficient than concatenating raw images) + combined_features = torch.cat([left_features, right_features], dim=-1) + # Feature fusion + fused_features = self.feature_fusion(combined_features) + + # Add state information if available + if state_obs is not None and self.state_obs_dim is not None: + final_features = torch.cat([fused_features, state_obs], dim=-1) + else: + final_features = fused_features + + # Predict actions + return self.mlp(final_features) + + def predict_pose(self, rgb_obs: torch.Tensor) -> torch.Tensor: + """Predict pose from rgb images and state observations.""" + # Ensure float32 for better performance + rgb_obs = rgb_obs.float() + left_features, right_features = self.get_features(rgb_obs) + left_pose = self.pose_mlp(left_features) + right_pose = self.pose_mlp(right_features) + return left_pose, right_pose diff --git a/examples/manipulation/grasp_env.py b/examples/manipulation/grasp_env.py index 740408a6a7..44f74a3899 100644 --- a/examples/manipulation/grasp_env.py +++ b/examples/manipulation/grasp_env.py @@ -2,22 +2,28 @@ import math from typing import Literal import genesis as gs -from genesis.utils.geom import xyz_to_quat, transform_quat_by_quat, transform_by_quat +from genesis.utils.geom import ( + xyz_to_quat, + transform_quat_by_quat, + transform_by_quat, +) class GraspEnv: def __init__( self, - num_envs, - env_cfg, - reward_cfg, - robot_cfg, - show_viewer=False, - ): - self.num_envs = num_envs + env_cfg: dict, + reward_cfg: dict, + robot_cfg: dict, + show_viewer: bool = False, + ) -> None: + self.num_envs = env_cfg["num_envs"] self.num_obs = env_cfg["num_obs"] self.num_privileged_obs = None self.num_actions = env_cfg["num_actions"] + self.image_width = env_cfg["image_resolution"][0] + self.image_height = env_cfg["image_resolution"][1] + self.rgb_image_shape = (3, self.image_height, self.image_width) self.device = gs.device self.ctrl_dt = env_cfg["ctrl_dt"] @@ -46,6 +52,9 @@ def __init__( ), profiling_options=gs.options.ProfilingOptions(show_FPS=False), show_viewer=show_viewer, + renderer=gs.options.renderers.BatchRenderer( + use_rasterizer=env_cfg["use_rasterizer"], + ), ) # == add ground == @@ -74,18 +83,24 @@ def __init__( ), ) - # == add camera == - if self.env_cfg["visualize_camera"]: - self.cam = self.scene.add_camera( - res=(1280, 720), - pos=(1.5, 0.0, 0.2), - lookat=(0, 0, 0.2), - fov=50, - GUI=True, - ) + # == add stero camera == + self.left_cam = self.scene.add_camera( + res=(self.image_width, self.image_height), + pos=(1.25, 0.3, 0.3), + lookat=(0.0, 0.0, 0.0), + fov=60, + GUI=False, + ) + self.right_cam = self.scene.add_camera( + res=(self.image_width, self.image_height), + pos=(1.25, -0.3, 0.3), + lookat=(0.0, 0.0, 0.0), + fov=60, + GUI=False, + ) # build - self.scene.build(n_envs=num_envs) + self.scene.build(n_envs=env_cfg["num_envs"]) # set pd gains (must be called after scene.build) self.robot.set_pd_gains() @@ -99,15 +114,16 @@ def __init__( self.keypoints_offset = self.get_keypoint_offsets(batch_size=self.num_envs, device=self.device, unit_length=0.5) # == init buffers == self._init_buffers() + self.reset() - def _init_buffers(self): + def _init_buffers(self) -> None: self.episode_length_buf = torch.zeros((self.num_envs,), device=gs.device, dtype=gs.tc_int) self.reset_buf = torch.zeros(self.num_envs, dtype=torch.bool, device=gs.device) self.goal_pose = torch.zeros(self.num_envs, 7, device=gs.device) self.extras = dict() self.extras["observations"] = dict() - def reset_idx(self, envs_idx): + def reset_idx(self, envs_idx: torch.Tensor) -> None: if len(envs_idx) == 0: return self.episode_length_buf[envs_idx] = 0 @@ -122,10 +138,9 @@ def reset_idx(self, envs_idx): random_z = torch.ones(num_reset, device=self.device) * 0.025 # 0.15 ~ 0.15 random_pos = torch.stack([random_x, random_y, random_z], dim=-1) - # randomly yaw the object + # downward facing quaternion to align with the hand q_downward = torch.tensor([0.0, 1.0, 0.0, 0.0], device=self.device).repeat(num_reset, 1) - PI = 3.1415926 - random_yaw = (torch.rand(num_reset, device=self.device) * 2 * PI - PI) * 0.25 + random_yaw = (torch.rand(num_reset, device=self.device) * 2 * math.pi - math.pi) * 0.25 q_yaw = torch.stack( [ torch.cos(random_yaw / 2), @@ -149,14 +164,14 @@ def reset_idx(self, envs_idx): ) self.episode_sums[key][envs_idx] = 0.0 - def reset(self): + def reset(self) -> tuple[torch.Tensor, dict]: self.reset_buf[:] = True self.reset_idx(torch.arange(self.num_envs, device=gs.device)) obs, self.extras = self.get_observations() - return obs, None + return obs, self.extras - def step(self, actions): + def step(self, actions: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, dict]: # update time self.episode_length_buf += 1 @@ -183,10 +198,10 @@ def step(self, actions): return obs, reward, self.reset_buf, self.extras - def get_privileged_observations(self): + def get_privileged_observations(self) -> None: return None - def is_episode_complete(self): + def is_episode_complete(self) -> torch.Tensor: time_out_buf = self.episode_length_buf > self.max_episode_length # check if the ee is in the valid position @@ -198,18 +213,19 @@ def is_episode_complete(self): self.extras["time_outs"][time_out_idx] = 1.0 return self.reset_buf.nonzero(as_tuple=True)[0] - def get_observations(self): + def get_observations(self) -> tuple[torch.Tensor, dict]: # Current end-effector pose finger_pos, finger_quat = ( self.robot.center_finger_pose[:, :3], self.robot.center_finger_pose[:, 3:7], ) obj_pos, obj_quat = self.object.get_pos(), self.object.get_quat() + # obs_components = [ finger_pos - obj_pos, # 3D position difference finger_quat, # current orientation (4D quaternion) - obj_pos, # goal pose (7D: pos + quat) - obj_quat, # goal pose (7D: pos + quat) + obj_pos, # goal position + obj_quat, # goal orientation (4D quaternion) ] obs_tensor = torch.cat(obs_components, dim=-1) self.extras["observations"]["critic"] = obs_tensor @@ -219,8 +235,37 @@ def rescale_action(self, action: torch.Tensor) -> torch.Tensor: rescaled_action = action * self.action_scales return rescaled_action + def get_depth_image(self, normalize: bool = True) -> torch.Tensor: + # Render depth image from the camera + _, depth, _, _ = self.batch_cam.render(rgb=False, depth=True) + depth = depth.permute(0, 3, 1, 2) # shape (B, 1, H, W) + if normalize: + depth = torch.clamp(depth, min=0.0, max=10) + depth = (depth - 0.0) / (10.0 - 0.0) # normalize to [0, 1] + return depth + + def get_stereo_rgb_images(self, normalize: bool = True) -> torch.Tensor: + rgb_left, _, _, _ = self.left_cam.render(rgb=True, depth=False) + rgb_right, _, _, _ = self.right_cam.render(rgb=True, depth=False) + + # Convert to proper format + rgb_left = rgb_left.permute(0, 3, 1, 2)[:, :3] # shape (B, 3, H, W) + rgb_right = rgb_right.permute(0, 3, 1, 2)[:, :3] # shape (B, 3, H, W) + + # Normalize if requested + if normalize: + rgb_left = torch.clamp(rgb_left, min=0.0, max=255.0) + rgb_left = (rgb_left - 0.0) / (255.0 - 0.0) + rgb_right = torch.clamp(rgb_right, min=0.0, max=255.0) + rgb_right = (rgb_right - 0.0) / (255.0 - 0.0) + + # Concatenate left and right rgb images along channel dimension + # Result: [B, 6, H, W] where channel 0 is left rgb, channel 1 is right rgb + stereo_rgb = torch.cat([rgb_left, rgb_right], dim=1) + return stereo_rgb + # ------------ begin reward functions---------------- - def _reward_keypoints(self): + def _reward_keypoints(self) -> torch.Tensor: keypoints_offset = self.keypoints_offset # there is a offset between the finger tip and the finger base frame finger_tip_z_offset = torch.tensor( @@ -237,23 +282,6 @@ def _reward_keypoints(self): dist = torch.norm(finger_pos_keypoints - object_pos_keypoints, p=2, dim=-1).sum(-1) return torch.exp(-dist) - def _reward_table_contact(self): - # Get current gripper DOF positions - gripper_dofs = self.robot._robot_entity.get_qpos()[:, self.robot._fingers_dof] - - # Expected gripper positions when not in contact (open gripper) - expected_open_pos = torch.tensor([0.04, 0.04], device=self.device).repeat(self.num_envs, 1) - - # Penalize when gripper DOFs are significantly different from expected open position - # This indicates contact with table or other obstacles - dof_error = torch.norm(gripper_dofs - expected_open_pos, dim=-1) - - # Apply penalty when error is above threshold (indicating contact) - contact_threshold = 0.001 # 1cm tolerance - contact_penalty = torch.where(dof_error > contact_threshold, -dof_error, torch.zeros_like(dof_error)) - - return contact_penalty - # ------------ end reward functions---------------- def _to_world_frame( @@ -268,7 +296,7 @@ def _to_world_frame( return world @staticmethod - def get_keypoint_offsets(batch_size, device, unit_length=0.5): + def get_keypoint_offsets(batch_size: int, device: str, unit_length: float = 0.5) -> torch.Tensor: """ Get uniformly-spaced keypoints along a line of unit length, centered at body center. """ @@ -290,22 +318,21 @@ def get_keypoint_offsets(batch_size, device, unit_length=0.5): ) return keypoint_offsets.unsqueeze(0).repeat(batch_size, 1, 1) - def grasp_and_lift_demo(self, render: bool = False): + def grasp_and_lift_demo(self) -> None: total_steps = 500 goal_pose = self.robot.ee_pose.clone() - + # lift pose (above the object) lift_height = 0.3 lift_pose = goal_pose.clone() lift_pose[:, 2] += lift_height - + # final pose (above the table) final_pose = goal_pose.clone() final_pose[:, 0] = 0.3 final_pose[:, 1] = 0.0 final_pose[:, 2] = 0.4 + # reset pose (home pose) reset_pose = torch.tensor([0.2, 0.0, 0.4, 0.0, 1.0, 0.0, 0.0], device=self.device).repeat(self.num_envs, 1) for i in range(total_steps): - if render: - self.cam.render() if i < total_steps / 4: # grasping self.robot.go_to_goal(goal_pose, open_gripper=False) elif i < total_steps / 2: # lifting @@ -335,6 +362,9 @@ def __init__(self, num_envs: int, scene: gs.Scene, args: dict, device: str = "cp ) self._robot_entity: gs.Entity = scene.add_entity(material=material, morph=morph) + self._gripper_open_dof = 0.04 + self._gripper_close_dof = 0.00 + self._ik_method: Literal["rel_pose", "dls"] = args["ik_method"] # == some buffer initialization == @@ -401,9 +431,9 @@ def apply_action(self, action: torch.Tensor, open_gripper: bool) -> None: raise ValueError(f"Invalid control mode: {self._ik_method}") # set gripper to open if open_gripper: - q_pos[:, self._fingers_dof] = +0.04 + q_pos[:, self._fingers_dof] = self._gripper_open_dof else: - q_pos[:, self._fingers_dof] = +0.02 + q_pos[:, self._fingers_dof] = self._gripper_close_dof self._robot_entity.control_dofs_position(position=q_pos) def _gs_ik(self, action: torch.Tensor) -> torch.Tensor: @@ -447,21 +477,11 @@ def go_to_goal(self, goal_pose: torch.Tensor, open_gripper: bool = True): dofs_idx_local=self._arm_dof_idx, ) if open_gripper: - q_pos[:, self._fingers_dof] = 0.04 + q_pos[:, self._fingers_dof] = self._gripper_open_dof else: - q_pos[:, self._fingers_dof] = +0.00 + q_pos[:, self._fingers_dof] = self._gripper_close_dof self._robot_entity.control_dofs_position(position=q_pos) - def open_gripper(self): - q_pos = self._robot_entity.get_qpos() - q_pos[:, self._fingers_dof] = 0.04 - self._robot_entity.set_qpos(q_pos) - - def close_gripper(self): - q_pos = self._robot_entity.get_qpos() - q_pos[:, self._fingers_dof] = +0.00 - self._robot_entity.set_qpos(q_pos) - @property def base_pos(self): return self._robot_entity.get_pos() diff --git a/examples/manipulation/grasp_eval.py b/examples/manipulation/grasp_eval.py index 2e7e67160d..1ab45bc72a 100644 --- a/examples/manipulation/grasp_eval.py +++ b/examples/manipulation/grasp_eval.py @@ -1,9 +1,12 @@ import argparse import os +import re import pickle from importlib import metadata import torch +import cv2 +import numpy as np try: try: @@ -19,63 +22,221 @@ import genesis as gs from grasp_env import GraspEnv +from behavior_cloning import BehaviorCloning + + +def load_rl_policy(env, train_cfg, log_dir): + """Load reinforcement learning policy.""" + runner = OnPolicyRunner(env, train_cfg, log_dir, device=gs.device) + + # Find the latest checkpoint + checkpoint_files = [f for f in os.listdir(log_dir) if re.match(r"model_\d+\.pt", f)] + if not checkpoint_files: + raise FileNotFoundError(f"No checkpoint files found in {log_dir}") + + last_ckpt = sorted(checkpoint_files)[-1] + runner.load(os.path.join(log_dir, last_ckpt)) + print(f"Loaded RL checkpoint from {last_ckpt}") + + return runner.get_inference_policy(device=gs.device) + + +def load_bc_policy(env, bc_cfg, log_dir): + """Load behavior cloning policy.""" + # Create behavior cloning instance + bc_runner = BehaviorCloning(env, bc_cfg, None, device=gs.device) + + # Find the latest checkpoint + checkpoint_files = [f for f in os.listdir(log_dir) if re.match(r"checkpoint_\d+\.pt", f)] + if not checkpoint_files: + raise FileNotFoundError(f"No checkpoint files found in {log_dir}") + + last_ckpt = sorted(checkpoint_files)[-1] + last_ckpt_file = os.path.join(log_dir, last_ckpt) + print(f"Loaded BC checkpoint from {last_ckpt_file}") + bc_runner.load(last_ckpt_file) + + return bc_runner._policy + + +def get_stereo_frame(env, step_count): + """Get stereo frame as numpy array.""" + # Get individual camera images + rgb_left, _, _, _ = env.left_cam.render(rgb=True, depth=False) + rgb_right, _, _, _ = env.right_cam.render(rgb=True, depth=False) + + # Convert to numpy arrays and format for OpenCV + left_img = rgb_left[0].cpu().numpy() # Take first environment + right_img = rgb_right[0].cpu().numpy() # Take first environment + + # Convert from RGB to BGR for OpenCV + left_img = cv2.cvtColor(left_img, cv2.COLOR_RGB2BGR) + right_img = cv2.cvtColor(right_img, cv2.COLOR_RGB2BGR) + + # Resize images for better display + scale = 2.0 + left_img = cv2.resize(left_img, (int(left_img.shape[1] * scale), int(left_img.shape[0] * scale))) + right_img = cv2.resize(right_img, (int(right_img.shape[1] * scale), int(right_img.shape[0] * scale))) + # Concatenate images horizontally (side by side) + stereo_img = np.hstack([left_img, right_img]) + # Add a vertical line separator between the two images + separator_x = left_img.shape[1] + cv2.line( + stereo_img, + (separator_x, 0), + (separator_x, stereo_img.shape[0]), + (255, 255, 255), + 2, + ) + return stereo_img + + +def display_stereo_images(env, step_count): + """Display left and right RGB images side by side in one window.""" + stereo_img = get_stereo_frame(env, step_count) + + # Display combined image + cv2.imshow("Stereo Cameras", stereo_img) + + # Wait for key press (1ms delay) + key = cv2.waitKey(1) + if key == 27: # ESC key + return False + return True + + +def save_frames_as_video(frames, video_path, fps=60): + """Save a list of frames as a video.""" + if not frames: + print("No frames to save!") + return + + # Get frame dimensions from the first frame + height, width = frames[0].shape[:2] + + # Create video writer + fourcc = cv2.VideoWriter_fourcc(*"mp4v") + video_writer = cv2.VideoWriter(video_path, fourcc, fps, (width, height)) + + if not video_writer.isOpened(): + print(f"Error: Could not open video writer for {video_path}") + return + + # Write frames to video + print(f"Saving {len(frames)} frames to {video_path}...") + for i, frame in enumerate(frames): + video_writer.write(frame) + if i % 30 == 0: # Progress indicator every 30 frames + print(f"Progress: {i + 1}/{len(frames)} frames") + + # Release video writer + video_writer.release() + print(f"Video saved successfully: {video_path}") def main(): parser = argparse.ArgumentParser() parser.add_argument("-e", "--exp_name", type=str, default="grasp") - parser.add_argument("--record", action="store_true", default=False) + parser.add_argument( + "--stage", + type=str, + default="rl", + choices=["rl", "bc"], + help="Model type: 'rl' for reinforcement learning, 'bc' for behavior cloning", + ) + parser.add_argument( + "--record", + action="store_true", + help="Record stereo images as video during evaluation", + ) + parser.add_argument( + "--video_path", + type=str, + default=None, + help="Path to save the video file (default: auto-generated)", + ) args = parser.parse_args() + # Set PyTorch default dtype to float32 for better performance + torch.set_default_dtype(torch.float32) + gs.init() - log_dir = f"logs/{args.exp_name}" - last_folder = sorted(os.listdir(log_dir))[-1] - env_cfg, reward_cfg, robot_cfg, train_cfg = pickle.load(open(f"logs/{args.exp_name}/{last_folder}/cfgs.pkl", "rb")) + log_dir = f"logs/{args.exp_name + '_' + args.stage}" + + # Load configurations + if args.stage == "rl": + # For RL, load the standard configs + env_cfg, reward_cfg, robot_cfg, rl_train_cfg, bc_train_cfg = pickle.load(open(f"{log_dir}/cfgs.pkl", "rb")) + else: + # For BC, we need to load the configs and create BC config + env_cfg, reward_cfg, robot_cfg, rl_train_cfg, bc_train_cfg = pickle.load(open(f"{log_dir}/cfgs.pkl", "rb")) - # visualize the target - env_cfg["visualize_target"] = True - # for video recording - env_cfg["visualize_camera"] = args.record # set the max FPS for visualization env_cfg["max_visualize_FPS"] = 60 # set the box collision env_cfg["box_collision"] = True # set the box fixed env_cfg["box_fixed"] = False + # set the number of envs for evaluation + env_cfg["num_envs"] = 10 env = GraspEnv( - num_envs=10, env_cfg=env_cfg, reward_cfg=reward_cfg, robot_cfg=robot_cfg, show_viewer=True, ) - runner = OnPolicyRunner(env, train_cfg, log_dir, device=gs.device) - last_ckpt = sorted(os.listdir(os.path.join(log_dir, last_folder)))[-1] - resume_path = os.path.join(log_dir, last_folder, last_ckpt) - print(f"Loading from {resume_path}") - runner.load(resume_path) - policy = runner.get_inference_policy(device=gs.device) + # Load the appropriate policy based on model type + if args.stage == "rl": + policy = load_rl_policy(env, rl_train_cfg, log_dir) + else: + policy = load_bc_policy(env, bc_train_cfg, log_dir) + policy.eval() + # Verify policy is float32 + print(f"Policy dtype: {next(policy.parameters()).dtype}") obs, _ = env.reset() + # Initialize frame list for recording + frames = [] max_sim_step = int(env_cfg["episode_length_s"] * env_cfg["max_visualize_FPS"]) + with torch.no_grad(): - if args.record: - env.cam.start_recording() - for _ in range(max_sim_step): - actions = policy(obs) - obs, rews, dones, infos = env.step(actions) - env.cam.render() - env.grasp_and_lift_demo(render=True) - env.cam.stop_recording(save_to_filename="video.mp4", fps=env_cfg["max_visualize_FPS"]) - else: - for _ in range(max_sim_step): + for step in range(max_sim_step): + if args.stage == "rl": actions = policy(obs) - obs, rews, dones, infos = env.step(actions) - env.grasp_and_lift_demo(render=False) + else: + # Get stereo grayscale images and ensure float32 + rgb_obs = env.get_stereo_rgb_images(normalize=True).float() + ee_pose = env.robot.ee_pose.float() + + actions = policy(rgb_obs, ee_pose) + + # Display stereo images for BC evaluation + if not display_stereo_images(env, step): + print("Evaluation stopped by user (ESC key pressed)") + break + + # Collect frame for video recording + if args.record: + frame = get_stereo_frame(env, step) + frames.append(frame) + + obs, rews, dones, infos = env.step(actions) + env.grasp_and_lift_demo() + + # Save video if recording was enabled + if args.record and frames: + video_path = f"{log_dir}/stereo_evaluation.mp4" + # Save frames as video + fps = env_cfg["max_visualize_FPS"] + save_frames_as_video(frames, video_path, fps) + + # Close OpenCV windows if BC evaluation + if args.stage == "bc": + cv2.destroyAllWindows() if __name__ == "__main__": @@ -83,9 +244,12 @@ def main(): """ # evaluation -python examples/manipulation/grasp_eval.py +# For reinforcement learning model: +python examples/manipulation/grasp_eval.py --stage rl + +# For behavior cloning model: +python examples/manipulation/grasp_eval.py --stage bc -# Note -If you experience slow performance or encounter other issues -during evaluation, try removing the --record option. +# With video recording: +python examples/manipulation/grasp_eval.py --stage bc --record """ diff --git a/examples/manipulation/grasp_train.py b/examples/manipulation/grasp_train.py index 54b2378d46..21e753dfc3 100644 --- a/examples/manipulation/grasp_train.py +++ b/examples/manipulation/grasp_train.py @@ -1,11 +1,9 @@ import argparse import os +import re import pickle -import shutil from importlib import metadata -import datetime - try: try: if metadata.version("rsl-rl"): @@ -15,8 +13,9 @@ raise ImportError except (metadata.PackageNotFoundError, ImportError) as e: raise ImportError("Please uninstall 'rsl_rl' and install 'rsl-rl-lib==2.2.4'.") from e -from rsl_rl.runners import OnPolicyRunner +from rsl_rl.runners import OnPolicyRunner +from behavior_cloning import BehaviorCloning import genesis as gs @@ -24,7 +23,8 @@ def get_train_cfg(exp_name, max_iterations): - train_cfg_dict = { + # stage 1: privileged reinforcement learning + rl_cfg_dict = { "algorithm": { "class_name": "PPO", "clip_param": 0.2, @@ -66,24 +66,75 @@ def get_train_cfg(exp_name, max_iterations): "seed": 1, } - return train_cfg_dict + # stage 2: vision-based behavior cloning + bc_cfg_dict = { + # Basic training parameters + "num_steps_per_env": 24, + "learning_rate": 0.001, + "num_epochs": 5, + "mini_batches_size": 512, + "max_grad_norm": 1.0, + # Network architecture + "policy": { + "vision_encoder": { + "conv_layers": [ + { + "in_channels": 3, # 3 channel for rgb image + "out_channels": 8, + "kernel_size": 3, + "stride": 1, + "padding": 1, + }, + { + "in_channels": 8, + "out_channels": 16, + "kernel_size": 3, + "stride": 2, + "padding": 1, + }, + { + "in_channels": 16, + "out_channels": 32, + "kernel_size": 3, + "stride": 2, + "padding": 1, + }, + ], + "pooling": "adaptive_avg", + }, + "action_head": { + "state_obs_dim": 7, # end-effector pose as additional state observation + "hidden_dims": [128, 128, 64], + }, + "pose_head": { + "hidden_dims": [64, 64], + }, + }, + # Training settings + "buffer_size": 1000, + "save_freq": 50, + "eval_freq": 50, + } + return rl_cfg_dict, bc_cfg_dict -def get_cfgs(): + +def get_task_cfgs(): env_cfg = { + "num_envs": 10, "num_obs": 14, "num_actions": 6, "action_scales": [0.05, 0.05, 0.05, 0.05, 0.05, 0.05], "episode_length_s": 3.0, "ctrl_dt": 0.01, - "box_size": [0.04, 0.04, 0.06], + "box_size": [0.08, 0.03, 0.06], "box_collision": False, "box_fixed": True, - "visualize_camera": False, + "image_resolution": (64, 64), + "use_rasterizer": True, } reward_scales = { "keypoints": 1.0, - "table_contact": -1.0, # Negative scale for penalty } # panda robot specific robot_cfg = { @@ -96,43 +147,62 @@ def get_cfgs(): return env_cfg, reward_scales, robot_cfg +def load_teacher_policy(env, rl_train_cfg, exp_name): + # load teacher policy + log_dir = f"logs/{exp_name + '_' + 'rl'}" + assert os.path.exists(log_dir), f"Log directory {log_dir} does not exist" + checkpoint_files = [f for f in os.listdir(log_dir) if re.match(r"model_\d+\.pt", f)] + last_ckpt = sorted(checkpoint_files)[-1] + assert last_ckpt is not None, "No checkpoint found in {log_dir}" + runner = OnPolicyRunner(env, rl_train_cfg, log_dir, device=gs.device) + runner.load(os.path.join(log_dir, last_ckpt)) + print(f"Loaded teacher policy from checkpoint {last_ckpt} from {log_dir}") + teacher_policy = runner.get_inference_policy(device=gs.device) + return teacher_policy + + def main(): parser = argparse.ArgumentParser() parser.add_argument("-e", "--exp_name", type=str, default="grasp") parser.add_argument("-v", "--vis", action="store_true", default=False) - parser.add_argument("-B", "--num_envs", type=int, default=200) - parser.add_argument("--max_iterations", type=int, default=500) + parser.add_argument("-B", "--num_envs", type=int, default=2048) + parser.add_argument("--max_iterations", type=int, default=300) + parser.add_argument("--stage", type=str, default="rl") args = parser.parse_args() + # === init === gs.init(logging_level="warning", precision="32") - log_dir = f"logs/{args.exp_name}/{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}" - env_cfg, reward_scales, robot_cfg = get_cfgs() - train_cfg = get_train_cfg(args.exp_name, args.max_iterations) + # === task cfgs and trainning algos cfgs === + env_cfg, reward_scales, robot_cfg = get_task_cfgs() + rl_train_cfg, bc_train_cfg = get_train_cfg(args.exp_name, args.max_iterations) - if os.path.exists(log_dir): - shutil.rmtree(log_dir) + # === log dir === + log_dir = f"logs/{args.exp_name + '_' + args.stage}" os.makedirs(log_dir, exist_ok=True) - if args.vis: - env_cfg["visualize_target"] = True - - pickle.dump( - [env_cfg, reward_scales, robot_cfg, train_cfg], - open(f"{log_dir}/cfgs.pkl", "wb"), - ) + with open(f"{log_dir}/cfgs.pkl", "wb") as f: + pickle.dump((env_cfg, reward_scales, robot_cfg, rl_train_cfg, bc_train_cfg), f) + # === env === + # BC only needs a small number of envs, e.g., 10 + env_cfg["num_envs"] = args.num_envs if args.stage == "rl" else 10 env = GraspEnv( - num_envs=args.num_envs, env_cfg=env_cfg, reward_cfg=reward_scales, robot_cfg=robot_cfg, show_viewer=args.vis, ) - runner = OnPolicyRunner(env, train_cfg, log_dir, device=gs.device) - - runner.learn(num_learning_iterations=args.max_iterations, init_at_random_ep_len=True) + # === runner === + if args.stage == "bc": + teacher_policy = load_teacher_policy(env, rl_train_cfg, args.exp_name) + bc_train_cfg["teacher_policy"] = teacher_policy + runner = BehaviorCloning(env, bc_train_cfg, teacher_policy, device=gs.device) + runner.learn(num_learning_iterations=args.max_iterations, log_dir=log_dir) + else: + runner = OnPolicyRunner(env, rl_train_cfg, log_dir, device=gs.device) + runner.learn(num_learning_iterations=args.max_iterations, init_at_random_ep_len=True) if __name__ == "__main__": From 2f43dcc5269d2398a0c6212e37ce5c84f4220a9f Mon Sep 17 00:00:00 2001 From: Yunlong Song Date: Sun, 3 Aug 2025 16:12:15 -0700 Subject: [PATCH 03/10] remove uv.lock 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deletions(-) diff --git a/examples/manipulation/behavior_cloning.py b/examples/manipulation/behavior_cloning.py index e1f6931472..e76d993a9a 100644 --- a/examples/manipulation/behavior_cloning.py +++ b/examples/manipulation/behavior_cloning.py @@ -6,6 +6,7 @@ import numpy as np from collections import deque +from collections.abc import Iterator from torch.utils.tensorboard import SummaryWriter @@ -37,7 +38,7 @@ def __init__(self, env, cfg: dict, teacher: nn.Module, device: str = "cpu"): img_shape=rgb_shape, state_dim=self._cfg["policy"]["action_head"]["state_obs_dim"], action_dim=action_dim, - device=self._device, + device=device, ) # Training state @@ -63,18 +64,18 @@ def learn(self, num_learning_iterations: int, log_dir: str) -> None: num_batches = 0 start_time = time.time() - generator = self._buffer.get_batches(self._cfg.get("mini_batches_size", 32), self._cfg["num_epochs"]) + generator = self._buffer.get_batches(self._cfg.get("num_mini_batches", 4), self._cfg["num_epochs"]) for batch in generator: # Forward pass for both action and pose prediction - pred_action = self._policy(batch["rgb_obs"].float(), batch["robot_pose"].float()) - pred_left_pose, pred_right_pose = self._policy.predict_pose(batch["rgb_obs"].float()) + pred_action = self._policy(batch["rgb_obs"], batch["robot_pose"]) + pred_left_pose, pred_right_pose = self._policy.predict_pose(batch["rgb_obs"]) # Compute action prediction loss - action_loss = F.mse_loss(pred_action, batch["actions"].float()) + action_loss = F.mse_loss(pred_action, batch["actions"]) # Compute pose estimation loss (position + orientation) - pose_left_loss = self._compute_pose_loss(pred_left_pose, batch["object_poses"].float()) - pose_right_loss = self._compute_pose_loss(pred_right_pose, batch["object_poses"].float()) + pose_left_loss = self._compute_pose_loss(pred_left_pose, batch["object_poses"]) + pose_right_loss = self._compute_pose_loss(pred_right_pose, batch["object_poses"]) pose_loss = pose_left_loss + pose_right_loss # Combined loss with weights @@ -227,7 +228,7 @@ def load_finetuned_model(self, path: str) -> None: class ExperienceBuffer: - """Experience buffer.""" + """A first-in-first-out buffer for experience replay.""" def __init__( self, @@ -238,20 +239,20 @@ def __init__( action_dim: int, device: str = "cpu", ): + self._num_envs = num_envs + self._max_size = max_size self._img_shape = img_shape self._state_dim = state_dim self._action_dim = action_dim - self._num_envs = num_envs - self._max_size = max_size self._device = device + self._ptr = 0 self._size = 0 - self._ptr = 0 # pointer to the next free slot in the buffer - # Initialize buffers - self._rgb_obs = torch.zeros(max_size, num_envs, *img_shape, device=device) - self._robot_pose = torch.zeros(max_size, num_envs, state_dim, device=device) - self._object_poses = torch.zeros(max_size, num_envs, 7, device=device) - self._actions = torch.zeros(max_size, num_envs, action_dim, device=device) + # Buffers for data + self._rgb_obs = torch.empty(max_size, num_envs, *img_shape, dtype=torch.float32, device=device) + self._robot_pose = torch.empty(max_size, num_envs, state_dim, dtype=torch.float32, device=device) + self._object_poses = torch.empty(max_size, num_envs, 7, dtype=torch.float32, device=device) + self._actions = torch.empty(max_size, num_envs, action_dim, dtype=torch.float32, device=device) def add( self, @@ -261,42 +262,38 @@ def add( actions: torch.Tensor, ) -> None: """Add experience to buffer.""" - ptr = self._ptr % self._max_size - self._rgb_obs[ptr].copy_(rgb_obs) - self._robot_pose[ptr].copy_(robot_pose) - self._object_poses[ptr].copy_(object_poses) - self._actions[ptr].copy_(actions) - self._ptr = self._ptr + 1 + self._ptr = (self._ptr + 1) % self._max_size + self._rgb_obs[self._ptr] = rgb_obs + self._robot_pose[self._ptr] = robot_pose + self._object_poses[self._ptr] = object_poses + self._actions[self._ptr] = actions self._size = min(self._size + 1, self._max_size) - def get_batches(self, mini_batches_size: int, num_epochs: int): + def get_batches(self, num_mini_batches: int, num_epochs: int) -> Iterator[dict[str, torch.Tensor]]: """Generate batches for training.""" - buffer_size = self._size * self._num_envs - indices = torch.randperm(buffer_size, device=self._device) # calculate the size of each mini-batch - num_batches = min(buffer_size // mini_batches_size, 10) + batch_size = self._size // num_mini_batches for _ in range(num_epochs): - for batch_idx in range(num_batches): - start = batch_idx * mini_batches_size - end = start + mini_batches_size - mb_indices = indices[start:end] + indices = torch.randperm(self._size) + for batch_idx in range(0, self._size, batch_size): + batch_indices = indices[batch_idx : batch_idx + batch_size] # Yield a mini-batch of data - batch = { - "rgb_obs": self._rgb_obs.view(-1, *self._img_shape)[mb_indices], - "robot_pose": self._robot_pose.view(-1, self._state_dim)[mb_indices], - "object_poses": self._object_poses.view(-1, 7)[mb_indices], - "actions": self._actions.view(-1, self._action_dim)[mb_indices], + yield { + "rgb_obs": self._rgb_obs[batch_indices].view(-1, *self._img_shape), + "robot_pose": self._robot_pose[batch_indices].view(-1, self._state_dim), + "object_poses": self._object_poses[batch_indices].view(-1, 7), + "actions": self._actions[batch_indices].view(-1, self._action_dim), } - yield batch def clear(self) -> None: """Clear the buffer.""" self._rgb_obs.zero_() self._robot_pose.zero_() + self._object_poses.zero_() self._actions.zero_() - self._size = 0 self._ptr = 0 + self._size = 0 def is_full(self) -> bool: """Check if buffer is full.""" @@ -343,9 +340,7 @@ def __init__(self, config: dict, action_dim: int): pose_mlp_cfg["output_dim"] = 7 self.pose_mlp = self._build_mlp(pose_mlp_cfg) - # Force float32 for better performance - self.float() - + @staticmethod def _build_cnn(self, config: dict) -> nn.Sequential: """Build CNN encoder for grayscale images.""" layers = [] @@ -372,7 +367,8 @@ def _build_cnn(self, config: dict) -> nn.Sequential: return nn.Sequential(*layers) - def _build_mlp(self, config: dict) -> nn.Sequential: + @staticmethod + def _build_mlp(config: dict) -> nn.Sequential: mlp_input_dim = config["input_dim"] layers = [] for hidden_dim in config["hidden_dims"]: @@ -393,11 +389,6 @@ def get_features(self, rgb_obs: torch.Tensor) -> torch.Tensor: def forward(self, rgb_obs: torch.Tensor, state_obs: torch.Tensor | None = None) -> dict: """Forward pass with shared stereo encoder for rgb images.""" - # Ensure float32 for better performance - rgb_obs = rgb_obs.float() - if state_obs is not None: - state_obs = state_obs.float() - # Get features left_features, right_features = self.get_features(rgb_obs) @@ -417,8 +408,6 @@ def forward(self, rgb_obs: torch.Tensor, state_obs: torch.Tensor | None = None) def predict_pose(self, rgb_obs: torch.Tensor) -> torch.Tensor: """Predict pose from rgb images and state observations.""" - # Ensure float32 for better performance - rgb_obs = rgb_obs.float() left_features, right_features = self.get_features(rgb_obs) left_pose = self.pose_mlp(left_features) right_pose = self.pose_mlp(right_features) diff --git a/examples/manipulation/grasp_env.py b/examples/manipulation/grasp_env.py index 44f74a3899..c645eb468e 100644 --- a/examples/manipulation/grasp_env.py +++ b/examples/manipulation/grasp_env.py @@ -1,6 +1,7 @@ import torch import math from typing import Literal + import genesis as gs from genesis.utils.geom import ( xyz_to_quat, @@ -8,6 +9,8 @@ transform_by_quat, ) +MAX_DEPTH = 10.0 + class GraspEnv: def __init__( @@ -223,9 +226,9 @@ def get_observations(self) -> tuple[torch.Tensor, dict]: # obs_components = [ finger_pos - obj_pos, # 3D position difference - finger_quat, # current orientation (4D quaternion) + finger_quat, # current orientation (w, x, y, z) obj_pos, # goal position - obj_quat, # goal orientation (4D quaternion) + obj_quat, # goal orientation (w, x, y, z) ] obs_tensor = torch.cat(obs_components, dim=-1) self.extras["observations"]["critic"] = obs_tensor @@ -237,16 +240,16 @@ def rescale_action(self, action: torch.Tensor) -> torch.Tensor: def get_depth_image(self, normalize: bool = True) -> torch.Tensor: # Render depth image from the camera - _, depth, _, _ = self.batch_cam.render(rgb=False, depth=True) + _, depth, _, _ = self.batch_cam.render(rgb=False, depth=True, segmentation=False, normal=False) depth = depth.permute(0, 3, 1, 2) # shape (B, 1, H, W) if normalize: - depth = torch.clamp(depth, min=0.0, max=10) - depth = (depth - 0.0) / (10.0 - 0.0) # normalize to [0, 1] + depth = torch.clamp(depth, min=0.0, max=MAX_DEPTH) + depth = (depth - 0.0) / (MAX_DEPTH - 0.0) # normalize to [0, 1] return depth def get_stereo_rgb_images(self, normalize: bool = True) -> torch.Tensor: - rgb_left, _, _, _ = self.left_cam.render(rgb=True, depth=False) - rgb_right, _, _, _ = self.right_cam.render(rgb=True, depth=False) + rgb_left, _, _, _ = self.left_cam.render(rgb=True, depth=False, segmentation=False, normal=False) + rgb_right, _, _, _ = self.right_cam.render(rgb=True, depth=False, segmentation=False, normal=False) # Convert to proper format rgb_left = rgb_left.permute(0, 3, 1, 2)[:, :3] # shape (B, 3, H, W) @@ -254,10 +257,8 @@ def get_stereo_rgb_images(self, normalize: bool = True) -> torch.Tensor: # Normalize if requested if normalize: - rgb_left = torch.clamp(rgb_left, min=0.0, max=255.0) - rgb_left = (rgb_left - 0.0) / (255.0 - 0.0) - rgb_right = torch.clamp(rgb_right, min=0.0, max=255.0) - rgb_right = (rgb_right - 0.0) / (255.0 - 0.0) + rgb_left = torch.clamp(rgb_left, min=0.0, max=255.0) / 255.0 + rgb_right = torch.clamp(rgb_right, min=0.0, max=255.0) / 255.0 # Concatenate left and right rgb images along channel dimension # Result: [B, 6, H, W] where channel 0 is left rgb, channel 1 is right rgb diff --git a/examples/manipulation/grasp_train.py b/examples/manipulation/grasp_train.py index 21e753dfc3..23f7325402 100644 --- a/examples/manipulation/grasp_train.py +++ b/examples/manipulation/grasp_train.py @@ -29,7 +29,7 @@ def get_train_cfg(exp_name, max_iterations): "class_name": "PPO", "clip_param": 0.2, "desired_kl": 0.01, - "entropy_coef": 0.00, + "entropy_coef": 0.0, "gamma": 0.99, "lam": 0.95, "learning_rate": 0.0003, @@ -72,7 +72,7 @@ def get_train_cfg(exp_name, max_iterations): "num_steps_per_env": 24, "learning_rate": 0.001, "num_epochs": 5, - "mini_batches_size": 512, + "num_mini_batches": 10, "max_grad_norm": 1.0, # Network architecture "policy": { @@ -210,5 +210,10 @@ def main(): """ # training -python examples/manipulation/grasp_train.py + +# to train the RL policy +python examples/manipulation/grasp_train.py --stage=rl + +# to train the BC policy (requires RL policy to be trained first) +python examples/manipulation/grasp_train.py --stage=bc """ From bdab23c478d7392bb33db972187206090e5a4eac Mon Sep 17 00:00:00 2001 From: Yunlong Song Date: Wed, 13 Aug 2025 09:43:29 -0700 Subject: [PATCH 05/10] several minor improvements --- examples/manipulation/grasp_env.py | 2 +- examples/manipulation/grasp_eval.py | 30 ++++++++++++++-------------- examples/manipulation/grasp_train.py | 17 ++++++++-------- 3 files changed, 25 insertions(+), 24 deletions(-) diff --git a/examples/manipulation/grasp_env.py b/examples/manipulation/grasp_env.py index c645eb468e..8063cd617e 100644 --- a/examples/manipulation/grasp_env.py +++ b/examples/manipulation/grasp_env.py @@ -9,7 +9,7 @@ transform_by_quat, ) -MAX_DEPTH = 10.0 +MAX_DEPTH = 10.0 # meters class GraspEnv: diff --git a/examples/manipulation/grasp_eval.py b/examples/manipulation/grasp_eval.py index 1ab45bc72a..2ab68548ae 100644 --- a/examples/manipulation/grasp_eval.py +++ b/examples/manipulation/grasp_eval.py @@ -3,6 +3,7 @@ import re import pickle from importlib import metadata +from pathlib import Path import torch import cv2 @@ -30,12 +31,12 @@ def load_rl_policy(env, train_cfg, log_dir): runner = OnPolicyRunner(env, train_cfg, log_dir, device=gs.device) # Find the latest checkpoint - checkpoint_files = [f for f in os.listdir(log_dir) if re.match(r"model_\d+\.pt", f)] + checkpoint_files = [f for f in log_dir.iterdir() if re.match(r"model_\d+\.pt", f.name)] if not checkpoint_files: raise FileNotFoundError(f"No checkpoint files found in {log_dir}") last_ckpt = sorted(checkpoint_files)[-1] - runner.load(os.path.join(log_dir, last_ckpt)) + runner.load(log_dir / last_ckpt) print(f"Loaded RL checkpoint from {last_ckpt}") return runner.get_inference_policy(device=gs.device) @@ -47,14 +48,13 @@ def load_bc_policy(env, bc_cfg, log_dir): bc_runner = BehaviorCloning(env, bc_cfg, None, device=gs.device) # Find the latest checkpoint - checkpoint_files = [f for f in os.listdir(log_dir) if re.match(r"checkpoint_\d+\.pt", f)] + checkpoint_files = [f for f in log_dir.iterdir() if re.match(r"checkpoint_\d+\.pt", f.name)] if not checkpoint_files: raise FileNotFoundError(f"No checkpoint files found in {log_dir}") last_ckpt = sorted(checkpoint_files)[-1] - last_ckpt_file = os.path.join(log_dir, last_ckpt) - print(f"Loaded BC checkpoint from {last_ckpt_file}") - bc_runner.load(last_ckpt_file) + print(f"Loaded BC checkpoint from {last_ckpt}") + bc_runner.load(log_dir / last_ckpt) return bc_runner._policy @@ -82,11 +82,11 @@ def get_stereo_frame(env, step_count): # Add a vertical line separator between the two images separator_x = left_img.shape[1] cv2.line( - stereo_img, - (separator_x, 0), - (separator_x, stereo_img.shape[0]), - (255, 255, 255), - 2, + image=stereo_img, + start_point=(separator_x, 0), + end_point=(separator_x, stereo_img.shape[0]), + color=(255, 255, 255), + thickness=2, ) return stereo_img @@ -162,15 +162,15 @@ def main(): gs.init() - log_dir = f"logs/{args.exp_name + '_' + args.stage}" + log_dir = Path("logs") / f"{args.exp_name + '_' + args.stage}" # Load configurations if args.stage == "rl": # For RL, load the standard configs - env_cfg, reward_cfg, robot_cfg, rl_train_cfg, bc_train_cfg = pickle.load(open(f"{log_dir}/cfgs.pkl", "rb")) + env_cfg, reward_cfg, robot_cfg, rl_train_cfg, bc_train_cfg = pickle.load(open(log_dir / "cfgs.pkl", "rb")) else: # For BC, we need to load the configs and create BC config - env_cfg, reward_cfg, robot_cfg, rl_train_cfg, bc_train_cfg = pickle.load(open(f"{log_dir}/cfgs.pkl", "rb")) + env_cfg, reward_cfg, robot_cfg, rl_train_cfg, bc_train_cfg = pickle.load(open(log_dir / "cfgs.pkl", "rb")) # set the max FPS for visualization env_cfg["max_visualize_FPS"] = 60 @@ -229,7 +229,7 @@ def main(): # Save video if recording was enabled if args.record and frames: - video_path = f"{log_dir}/stereo_evaluation.mp4" + video_path = log_dir / "stereo_evaluation.mp4" # Save frames as video fps = env_cfg["max_visualize_FPS"] save_frames_as_video(frames, video_path, fps) diff --git a/examples/manipulation/grasp_train.py b/examples/manipulation/grasp_train.py index 23f7325402..57654fac54 100644 --- a/examples/manipulation/grasp_train.py +++ b/examples/manipulation/grasp_train.py @@ -3,6 +3,7 @@ import re import pickle from importlib import metadata +from pathlib import Path try: try: @@ -149,13 +150,13 @@ def get_task_cfgs(): def load_teacher_policy(env, rl_train_cfg, exp_name): # load teacher policy - log_dir = f"logs/{exp_name + '_' + 'rl'}" - assert os.path.exists(log_dir), f"Log directory {log_dir} does not exist" - checkpoint_files = [f for f in os.listdir(log_dir) if re.match(r"model_\d+\.pt", f)] + log_dir = Path("logs") / f"{exp_name + '_' + 'rl'}" + assert log_dir.exists(), f"Log directory {log_dir} does not exist" + checkpoint_files = [f for f in log_dir.iterdir() if re.match(r"model_\d+\.pt", f.name)] last_ckpt = sorted(checkpoint_files)[-1] - assert last_ckpt is not None, "No checkpoint found in {log_dir}" + assert last_ckpt is not None, f"No checkpoint found in {log_dir}" runner = OnPolicyRunner(env, rl_train_cfg, log_dir, device=gs.device) - runner.load(os.path.join(log_dir, last_ckpt)) + runner.load(log_dir / last_ckpt) print(f"Loaded teacher policy from checkpoint {last_ckpt} from {log_dir}") teacher_policy = runner.get_inference_policy(device=gs.device) return teacher_policy @@ -178,10 +179,10 @@ def main(): rl_train_cfg, bc_train_cfg = get_train_cfg(args.exp_name, args.max_iterations) # === log dir === - log_dir = f"logs/{args.exp_name + '_' + args.stage}" - os.makedirs(log_dir, exist_ok=True) + log_dir = Path("logs") / f"{args.exp_name + '_' + args.stage}" + log_dir.mkdir(parents=True, exist_ok=True) - with open(f"{log_dir}/cfgs.pkl", "wb") as f: + with open(log_dir / "cfgs.pkl", "wb") as f: pickle.dump((env_cfg, reward_scales, robot_cfg, rl_train_cfg, bc_train_cfg), f) # === env === From d6f44e85ecabcadfb54e001922c5c795b2b2a9a1 Mon Sep 17 00:00:00 2001 From: Yunlong Song Date: Wed, 13 Aug 2025 10:15:51 -0700 Subject: [PATCH 06/10] several minor improvements --- examples/manipulation/behavior_cloning.py | 2 +- examples/manipulation/grasp_env.py | 1 + examples/manipulation/grasp_eval.py | 10 +++++----- examples/manipulation/grasp_train.py | 2 +- 4 files changed, 8 insertions(+), 7 deletions(-) diff --git a/examples/manipulation/behavior_cloning.py b/examples/manipulation/behavior_cloning.py index e76d993a9a..eae421622b 100644 --- a/examples/manipulation/behavior_cloning.py +++ b/examples/manipulation/behavior_cloning.py @@ -341,7 +341,7 @@ def __init__(self, config: dict, action_dim: int): self.pose_mlp = self._build_mlp(pose_mlp_cfg) @staticmethod - def _build_cnn(self, config: dict) -> nn.Sequential: + def _build_cnn(config: dict) -> nn.Sequential: """Build CNN encoder for grayscale images.""" layers = [] diff --git a/examples/manipulation/grasp_env.py b/examples/manipulation/grasp_env.py index 8063cd617e..1d7a4ebcb0 100644 --- a/examples/manipulation/grasp_env.py +++ b/examples/manipulation/grasp_env.py @@ -143,6 +143,7 @@ def reset_idx(self, envs_idx: torch.Tensor) -> None: # downward facing quaternion to align with the hand q_downward = torch.tensor([0.0, 1.0, 0.0, 0.0], device=self.device).repeat(num_reset, 1) + # randomly yaw the object random_yaw = (torch.rand(num_reset, device=self.device) * 2 * math.pi - math.pi) * 0.25 q_yaw = torch.stack( [ diff --git a/examples/manipulation/grasp_eval.py b/examples/manipulation/grasp_eval.py index 2ab68548ae..c29127abb8 100644 --- a/examples/manipulation/grasp_eval.py +++ b/examples/manipulation/grasp_eval.py @@ -36,7 +36,7 @@ def load_rl_policy(env, train_cfg, log_dir): raise FileNotFoundError(f"No checkpoint files found in {log_dir}") last_ckpt = sorted(checkpoint_files)[-1] - runner.load(log_dir / last_ckpt) + runner.load(last_ckpt) print(f"Loaded RL checkpoint from {last_ckpt}") return runner.get_inference_policy(device=gs.device) @@ -54,7 +54,7 @@ def load_bc_policy(env, bc_cfg, log_dir): last_ckpt = sorted(checkpoint_files)[-1] print(f"Loaded BC checkpoint from {last_ckpt}") - bc_runner.load(log_dir / last_ckpt) + bc_runner.load(last_ckpt) return bc_runner._policy @@ -82,9 +82,9 @@ def get_stereo_frame(env, step_count): # Add a vertical line separator between the two images separator_x = left_img.shape[1] cv2.line( - image=stereo_img, - start_point=(separator_x, 0), - end_point=(separator_x, stereo_img.shape[0]), + img=stereo_img, + pt1=(separator_x, 0), + pt2=(separator_x, stereo_img.shape[0]), color=(255, 255, 255), thickness=2, ) diff --git a/examples/manipulation/grasp_train.py b/examples/manipulation/grasp_train.py index 57654fac54..8782f9eb2a 100644 --- a/examples/manipulation/grasp_train.py +++ b/examples/manipulation/grasp_train.py @@ -156,7 +156,7 @@ def load_teacher_policy(env, rl_train_cfg, exp_name): last_ckpt = sorted(checkpoint_files)[-1] assert last_ckpt is not None, f"No checkpoint found in {log_dir}" runner = OnPolicyRunner(env, rl_train_cfg, log_dir, device=gs.device) - runner.load(log_dir / last_ckpt) + runner.load(last_ckpt) print(f"Loaded teacher policy from checkpoint {last_ckpt} from {log_dir}") teacher_policy = runner.get_inference_policy(device=gs.device) return teacher_policy From dc4be797498a3ee525147871ad19bc0f79378732 Mon Sep 17 00:00:00 2001 From: Yunlong Song Date: Wed, 13 Aug 2025 10:55:03 -0700 Subject: [PATCH 07/10] get stereo image directly --- examples/manipulation/grasp_eval.py | 36 +++++++++++++--------------- examples/manipulation/grasp_train.py | 5 +++- 2 files changed, 21 insertions(+), 20 deletions(-) diff --git a/examples/manipulation/grasp_eval.py b/examples/manipulation/grasp_eval.py index c29127abb8..feeefbdda4 100644 --- a/examples/manipulation/grasp_eval.py +++ b/examples/manipulation/grasp_eval.py @@ -35,7 +35,10 @@ def load_rl_policy(env, train_cfg, log_dir): if not checkpoint_files: raise FileNotFoundError(f"No checkpoint files found in {log_dir}") - last_ckpt = sorted(checkpoint_files)[-1] + try: + *_, last_ckpt = sorted(checkpoint_files) + except ValueError as e: + raise FileNotFoundError(f"No checkpoint files found in {log_dir}") from e runner.load(last_ckpt) print(f"Loaded RL checkpoint from {last_ckpt}") @@ -52,7 +55,10 @@ def load_bc_policy(env, bc_cfg, log_dir): if not checkpoint_files: raise FileNotFoundError(f"No checkpoint files found in {log_dir}") - last_ckpt = sorted(checkpoint_files)[-1] + try: + *_, last_ckpt = sorted(checkpoint_files) + except ValueError as e: + raise FileNotFoundError(f"No checkpoint files found in {log_dir}") from e print(f"Loaded BC checkpoint from {last_ckpt}") bc_runner.load(last_ckpt) @@ -61,34 +67,26 @@ def load_bc_policy(env, bc_cfg, log_dir): def get_stereo_frame(env, step_count): """Get stereo frame as numpy array.""" - # Get individual camera images - rgb_left, _, _, _ = env.left_cam.render(rgb=True, depth=False) - rgb_right, _, _, _ = env.right_cam.render(rgb=True, depth=False) - - # Convert to numpy arrays and format for OpenCV - left_img = rgb_left[0].cpu().numpy() # Take first environment - right_img = rgb_right[0].cpu().numpy() # Take first environment + # Get stacked stereo rgb image (B, 6, H, W) + stacked_stereo_rgb = env.get_stereo_rgb_images(normalize=False).cpu().numpy()[0] + stacked_stereo_rgb = stacked_stereo_rgb.transpose(1, 2, 0) - # Convert from RGB to BGR for OpenCV + # Split stacked stereo rgb image into left and right images + left_img, right_img = np.split(stacked_stereo_rgb, 2, axis=2) left_img = cv2.cvtColor(left_img, cv2.COLOR_RGB2BGR) right_img = cv2.cvtColor(right_img, cv2.COLOR_RGB2BGR) + stereo_rgb_img = np.concatenate([left_img, right_img], axis=1) - # Resize images for better display - scale = 2.0 - left_img = cv2.resize(left_img, (int(left_img.shape[1] * scale), int(left_img.shape[0] * scale))) - right_img = cv2.resize(right_img, (int(right_img.shape[1] * scale), int(right_img.shape[0] * scale))) - # Concatenate images horizontally (side by side) - stereo_img = np.hstack([left_img, right_img]) # Add a vertical line separator between the two images separator_x = left_img.shape[1] cv2.line( - img=stereo_img, + img=stereo_rgb_img, pt1=(separator_x, 0), - pt2=(separator_x, stereo_img.shape[0]), + pt2=(separator_x, stereo_rgb_img.shape[0]), color=(255, 255, 255), thickness=2, ) - return stereo_img + return stereo_rgb_img def display_stereo_images(env, step_count): diff --git a/examples/manipulation/grasp_train.py b/examples/manipulation/grasp_train.py index 8782f9eb2a..abe9445aa6 100644 --- a/examples/manipulation/grasp_train.py +++ b/examples/manipulation/grasp_train.py @@ -153,7 +153,10 @@ def load_teacher_policy(env, rl_train_cfg, exp_name): log_dir = Path("logs") / f"{exp_name + '_' + 'rl'}" assert log_dir.exists(), f"Log directory {log_dir} does not exist" checkpoint_files = [f for f in log_dir.iterdir() if re.match(r"model_\d+\.pt", f.name)] - last_ckpt = sorted(checkpoint_files)[-1] + try: + *_, last_ckpt = sorted(checkpoint_files) + except ValueError as e: + raise FileNotFoundError(f"No checkpoint files found in {log_dir}") from e assert last_ckpt is not None, f"No checkpoint found in {log_dir}" runner = OnPolicyRunner(env, rl_train_cfg, log_dir, device=gs.device) runner.load(last_ckpt) From b0e657182435905d84007dbc1338bf484aafd3d8 Mon Sep 17 00:00:00 2001 From: Yunlong Song Date: Wed, 20 Aug 2025 18:20:15 -0700 Subject: [PATCH 08/10] minor fixing --- examples/manipulation/behavior_cloning.py | 43 +++++++++++++---------- examples/manipulation/grasp_env.py | 20 +++++------ examples/manipulation/grasp_eval.py | 4 +-- examples/manipulation/grasp_train.py | 2 ++ 4 files changed, 39 insertions(+), 30 deletions(-) diff --git a/examples/manipulation/behavior_cloning.py b/examples/manipulation/behavior_cloning.py index eae421622b..faf01e9f36 100644 --- a/examples/manipulation/behavior_cloning.py +++ b/examples/manipulation/behavior_cloning.py @@ -1,13 +1,12 @@ import os -import torch import time -import torch.nn as nn -import torch.nn.functional as F - -import numpy as np from collections import deque from collections.abc import Iterator +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F from torch.utils.tensorboard import SummaryWriter @@ -39,6 +38,7 @@ def __init__(self, env, cfg: dict, teacher: nn.Module, device: str = "cpu"): state_dim=self._cfg["policy"]["action_head"]["state_obs_dim"], action_dim=action_dim, device=device, + dtype=self._policy.dtype, ) # Training state @@ -95,12 +95,15 @@ def learn(self, num_learning_iterations: int, log_dir: str) -> None: backward_time = end_time - start_time # Compute average losses - avg_action_loss = total_action_loss / num_batches if num_batches > 0 else 0.0 - avg_pose_loss = total_pose_loss / num_batches if num_batches > 0 else 0.0 + if num_batches == 0: + raise ValueError("No batches collected") + else: + avg_action_loss = total_action_loss / num_batches + avg_pose_loss = total_pose_loss / num_batches fps = (self._num_steps_per_env * self._env.num_envs) / (forward_time) # Logging - if (it + 1) % 10 == 0: + if (it + 1) % self._cfg["log_freq"] == 0: current_lr = self._optimizer.param_groups[0]["lr"] tf_writer.add_scalar("loss/action_loss", avg_action_loss, it) @@ -128,7 +131,7 @@ def learn(self, num_learning_iterations: int, log_dir: str) -> None: tf_writer.add_scalar("reward/mean", np.mean(self._rewbuffer), it) # Save checkpoints periodically - if (it + 1) % 50 == 0: + if (it + 1) % self._cfg["save_freq"] == 0: self.save(os.path.join(log_dir, f"checkpoint_{it + 1:04d}.pt")) tf_writer.close() @@ -150,6 +153,9 @@ def _compute_pose_loss(self, pred_poses: torch.Tensor, target_poses: torch.Tenso target_quat = F.normalize(target_quat, p=2, dim=1) # Quaternion distance: 1 - |dot(q1, q2)| + # Note: we use this as a proxy for the actual distance between two quaternions + # because the impact of the orientation loss (auxiliary task) is not significant + # compared to the action loss (main task) quat_dot = torch.sum(pred_quat * target_quat, dim=1) quat_loss = torch.mean(1.0 - torch.abs(quat_dot)) @@ -238,6 +244,7 @@ def __init__( state_dim: int, action_dim: int, device: str = "cpu", + dtype: torch.dtype | None = None, ): self._num_envs = num_envs self._max_size = max_size @@ -249,10 +256,10 @@ def __init__( self._size = 0 # Buffers for data - self._rgb_obs = torch.empty(max_size, num_envs, *img_shape, dtype=torch.float32, device=device) - self._robot_pose = torch.empty(max_size, num_envs, state_dim, dtype=torch.float32, device=device) - self._object_poses = torch.empty(max_size, num_envs, 7, dtype=torch.float32, device=device) - self._actions = torch.empty(max_size, num_envs, action_dim, dtype=torch.float32, device=device) + self._rgb_obs = torch.empty(max_size, num_envs, *img_shape, dtype=dtype, device=device) + self._robot_pose = torch.empty(max_size, num_envs, state_dim, dtype=dtype, device=device) + self._object_poses = torch.empty(max_size, num_envs, 7, dtype=dtype, device=device) + self._actions = torch.empty(max_size, num_envs, action_dim, dtype=dtype, device=device) def add( self, @@ -280,10 +287,10 @@ def get_batches(self, num_mini_batches: int, num_epochs: int) -> Iterator[dict[s # Yield a mini-batch of data yield { - "rgb_obs": self._rgb_obs[batch_indices].view(-1, *self._img_shape), - "robot_pose": self._robot_pose[batch_indices].view(-1, self._state_dim), - "object_poses": self._object_poses[batch_indices].view(-1, 7), - "actions": self._actions[batch_indices].view(-1, self._action_dim), + "rgb_obs": self._rgb_obs[batch_indices].reshape(-1, *self._img_shape), + "robot_pose": self._robot_pose[batch_indices].reshape(-1, self._state_dim), + "object_poses": self._object_poses[batch_indices].reshape(-1, 7), + "actions": self._actions[batch_indices].reshape(-1, self._action_dim), } def clear(self) -> None: @@ -377,7 +384,7 @@ def _build_mlp(config: dict) -> nn.Sequential: layers.append(nn.Linear(mlp_input_dim, config["output_dim"])) return nn.Sequential(*layers) - def get_features(self, rgb_obs: torch.Tensor) -> torch.Tensor: + def get_features(self, rgb_obs: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: # Split stereo rgb images left_rgb = rgb_obs[:, 0:3] # First 3 channels (RGB) right_rgb = rgb_obs[:, 3:6] # Last 3 channels (RGB) diff --git a/examples/manipulation/grasp_env.py b/examples/manipulation/grasp_env.py index 1d7a4ebcb0..5ed5cc0e8f 100644 --- a/examples/manipulation/grasp_env.py +++ b/examples/manipulation/grasp_env.py @@ -40,24 +40,24 @@ def __init__( # == setup scene == self.scene = gs.Scene( sim_options=gs.options.SimOptions(dt=self.ctrl_dt, substeps=2), - viewer_options=gs.options.ViewerOptions( - max_FPS=int(0.5 / self.ctrl_dt), - camera_pos=(2.0, 0.0, 2.5), - camera_lookat=(0.0, 0.0, 0.5), - camera_fov=40, - ), - vis_options=gs.options.VisOptions(rendered_envs_idx=list(range(10))), rigid_options=gs.options.RigidOptions( dt=self.ctrl_dt, constraint_solver=gs.constraint_solver.Newton, enable_collision=True, enable_joint_limit=True, ), + vis_options=gs.options.VisOptions(rendered_envs_idx=list(range(10))), + viewer_options=gs.options.ViewerOptions( + max_FPS=int(0.5 / self.ctrl_dt), + camera_pos=(2.0, 0.0, 2.5), + camera_lookat=(0.0, 0.0, 0.5), + camera_fov=40, + ), profiling_options=gs.options.ProfilingOptions(show_FPS=False), - show_viewer=show_viewer, renderer=gs.options.renderers.BatchRenderer( use_rasterizer=env_cfg["use_rasterizer"], ), + show_viewer=show_viewer, ) # == add ground == @@ -92,14 +92,14 @@ def __init__( pos=(1.25, 0.3, 0.3), lookat=(0.0, 0.0, 0.0), fov=60, - GUI=False, + GUI=self.env_cfg["visualize_camera"], ) self.right_cam = self.scene.add_camera( res=(self.image_width, self.image_height), pos=(1.25, -0.3, 0.3), lookat=(0.0, 0.0, 0.0), fov=60, - GUI=False, + GUI=self.env_cfg["visualize_camera"], ) # build diff --git a/examples/manipulation/grasp_eval.py b/examples/manipulation/grasp_eval.py index feeefbdda4..f55bb41113 100644 --- a/examples/manipulation/grasp_eval.py +++ b/examples/manipulation/grasp_eval.py @@ -73,8 +73,8 @@ def get_stereo_frame(env, step_count): # Split stacked stereo rgb image into left and right images left_img, right_img = np.split(stacked_stereo_rgb, 2, axis=2) - left_img = cv2.cvtColor(left_img, cv2.COLOR_RGB2BGR) - right_img = cv2.cvtColor(right_img, cv2.COLOR_RGB2BGR) + cv2.cvtColor(left_img, left_img, cv2.COLOR_RGB2BGR) + cv2.cvtColor(right_img, right_img, cv2.COLOR_RGB2BGR) stereo_rgb_img = np.concatenate([left_img, right_img], axis=1) # Add a vertical line separator between the two images diff --git a/examples/manipulation/grasp_train.py b/examples/manipulation/grasp_train.py index abe9445aa6..07b488ae67 100644 --- a/examples/manipulation/grasp_train.py +++ b/examples/manipulation/grasp_train.py @@ -113,6 +113,7 @@ def get_train_cfg(exp_name, max_iterations): }, # Training settings "buffer_size": 1000, + "log_freq": 10, "save_freq": 50, "eval_freq": 50, } @@ -133,6 +134,7 @@ def get_task_cfgs(): "box_fixed": True, "image_resolution": (64, 64), "use_rasterizer": True, + "visualize_camera": False, } reward_scales = { "keypoints": 1.0, From c3e6ea53ac594736554608c1770b8a60a30a49a2 Mon Sep 17 00:00:00 2001 From: Yunlong Song Date: Thu, 21 Aug 2025 12:23:05 -0700 Subject: [PATCH 09/10] minor fixing --- examples/manipulation/behavior_cloning.py | 5 +++ examples/manipulation/grasp_env.py | 11 ------ examples/manipulation/grasp_eval.py | 48 ++++++++++------------- 3 files changed, 25 insertions(+), 39 deletions(-) diff --git a/examples/manipulation/behavior_cloning.py b/examples/manipulation/behavior_cloning.py index faf01e9f36..578c1a694c 100644 --- a/examples/manipulation/behavior_cloning.py +++ b/examples/manipulation/behavior_cloning.py @@ -347,6 +347,11 @@ def __init__(self, config: dict, action_dim: int): pose_mlp_cfg["output_dim"] = 7 self.pose_mlp = self._build_mlp(pose_mlp_cfg) + @property + def dtype(self): + """Get the dtype of the policy's parameters.""" + return next(self.parameters()).dtype + @staticmethod def _build_cnn(config: dict) -> nn.Sequential: """Build CNN encoder for grayscale images.""" diff --git a/examples/manipulation/grasp_env.py b/examples/manipulation/grasp_env.py index 5ed5cc0e8f..274f03ea12 100644 --- a/examples/manipulation/grasp_env.py +++ b/examples/manipulation/grasp_env.py @@ -9,8 +9,6 @@ transform_by_quat, ) -MAX_DEPTH = 10.0 # meters - class GraspEnv: def __init__( @@ -239,15 +237,6 @@ def rescale_action(self, action: torch.Tensor) -> torch.Tensor: rescaled_action = action * self.action_scales return rescaled_action - def get_depth_image(self, normalize: bool = True) -> torch.Tensor: - # Render depth image from the camera - _, depth, _, _ = self.batch_cam.render(rgb=False, depth=True, segmentation=False, normal=False) - depth = depth.permute(0, 3, 1, 2) # shape (B, 1, H, W) - if normalize: - depth = torch.clamp(depth, min=0.0, max=MAX_DEPTH) - depth = (depth - 0.0) / (MAX_DEPTH - 0.0) # normalize to [0, 1] - return depth - def get_stereo_rgb_images(self, normalize: bool = True) -> torch.Tensor: rgb_left, _, _, _ = self.left_cam.render(rgb=True, depth=False, segmentation=False, normal=False) rgb_right, _, _, _ = self.right_cam.render(rgb=True, depth=False, segmentation=False, normal=False) diff --git a/examples/manipulation/grasp_eval.py b/examples/manipulation/grasp_eval.py index f55bb41113..a783c7aa25 100644 --- a/examples/manipulation/grasp_eval.py +++ b/examples/manipulation/grasp_eval.py @@ -1,5 +1,4 @@ import argparse -import os import re import pickle from importlib import metadata @@ -17,7 +16,9 @@ if metadata.version("rsl-rl-lib") != "2.2.4": raise ImportError except (metadata.PackageNotFoundError, ImportError) as e: - raise ImportError("Please uninstall 'rsl_rl' and install 'rsl-rl-lib==2.2.4'.") from e + raise ImportError( + "Please uninstall 'rsl_rl' and install 'rsl-rl-lib==2.2.4'." + ) from e from rsl_rl.runners import OnPolicyRunner import genesis as gs @@ -31,7 +32,9 @@ def load_rl_policy(env, train_cfg, log_dir): runner = OnPolicyRunner(env, train_cfg, log_dir, device=gs.device) # Find the latest checkpoint - checkpoint_files = [f for f in log_dir.iterdir() if re.match(r"model_\d+\.pt", f.name)] + checkpoint_files = [ + f for f in log_dir.iterdir() if re.match(r"model_\d+\.pt", f.name) + ] if not checkpoint_files: raise FileNotFoundError(f"No checkpoint files found in {log_dir}") @@ -51,7 +54,9 @@ def load_bc_policy(env, bc_cfg, log_dir): bc_runner = BehaviorCloning(env, bc_cfg, None, device=gs.device) # Find the latest checkpoint - checkpoint_files = [f for f in log_dir.iterdir() if re.match(r"checkpoint_\d+\.pt", f.name)] + checkpoint_files = [ + f for f in log_dir.iterdir() if re.match(r"checkpoint_\d+\.pt", f.name) + ] if not checkpoint_files: raise FileNotFoundError(f"No checkpoint files found in {log_dir}") @@ -73,9 +78,10 @@ def get_stereo_frame(env, step_count): # Split stacked stereo rgb image into left and right images left_img, right_img = np.split(stacked_stereo_rgb, 2, axis=2) - cv2.cvtColor(left_img, left_img, cv2.COLOR_RGB2BGR) - cv2.cvtColor(right_img, right_img, cv2.COLOR_RGB2BGR) - stereo_rgb_img = np.concatenate([left_img, right_img], axis=1) + # Convert RGB to BGR for OpenCV display + left_img_bgr = cv2.cvtColor(left_img, cv2.COLOR_RGB2BGR) + right_img_bgr = cv2.cvtColor(right_img, cv2.COLOR_RGB2BGR) + stereo_rgb_img = np.concatenate([left_img_bgr, right_img_bgr], axis=1) # Add a vertical line separator between the two images separator_x = left_img.shape[1] @@ -89,20 +95,6 @@ def get_stereo_frame(env, step_count): return stereo_rgb_img -def display_stereo_images(env, step_count): - """Display left and right RGB images side by side in one window.""" - stereo_img = get_stereo_frame(env, step_count) - - # Display combined image - cv2.imshow("Stereo Cameras", stereo_img) - - # Wait for key press (1ms delay) - key = cv2.waitKey(1) - if key == 27: # ESC key - return False - return True - - def save_frames_as_video(frames, video_path, fps=60): """Save a list of frames as a video.""" if not frames: @@ -165,11 +157,16 @@ def main(): # Load configurations if args.stage == "rl": # For RL, load the standard configs - env_cfg, reward_cfg, robot_cfg, rl_train_cfg, bc_train_cfg = pickle.load(open(log_dir / "cfgs.pkl", "rb")) + env_cfg, reward_cfg, robot_cfg, rl_train_cfg, bc_train_cfg = pickle.load( + open(log_dir / "cfgs.pkl", "rb") + ) else: # For BC, we need to load the configs and create BC config - env_cfg, reward_cfg, robot_cfg, rl_train_cfg, bc_train_cfg = pickle.load(open(log_dir / "cfgs.pkl", "rb")) + env_cfg, reward_cfg, robot_cfg, rl_train_cfg, bc_train_cfg = pickle.load( + open(log_dir / "cfgs.pkl", "rb") + ) + env_cfg["visualize_camera"] = True # set the max FPS for visualization env_cfg["max_visualize_FPS"] = 60 # set the box collision @@ -212,11 +209,6 @@ def main(): actions = policy(rgb_obs, ee_pose) - # Display stereo images for BC evaluation - if not display_stereo_images(env, step): - print("Evaluation stopped by user (ESC key pressed)") - break - # Collect frame for video recording if args.record: frame = get_stereo_frame(env, step) From ad6778dc73c53059c90808777a716813157c48f8 Mon Sep 17 00:00:00 2001 From: Yunlong Song Date: Mon, 1 Sep 2025 16:28:25 -0700 Subject: [PATCH 10/10] remove opencv display and use the default genesis render --- examples/manipulation/grasp_env.py | 9 +++ examples/manipulation/grasp_eval.py | 113 ++++++---------------------- 2 files changed, 30 insertions(+), 92 deletions(-) diff --git a/examples/manipulation/grasp_env.py b/examples/manipulation/grasp_env.py index 274f03ea12..02a39b5ea3 100644 --- a/examples/manipulation/grasp_env.py +++ b/examples/manipulation/grasp_env.py @@ -83,6 +83,15 @@ def __init__( ), ), ) + if self.env_cfg["visualize_camera"]: + self.vis_cam = self.scene.add_camera( + res=(1280, 720), + pos=(1.5, 0.0, 0.2), + lookat=(0.0, 0.0, 0.2), + fov=60, + GUI=self.env_cfg["visualize_camera"], + debug=True, + ) # == add stero camera == self.left_cam = self.scene.add_camera( diff --git a/examples/manipulation/grasp_eval.py b/examples/manipulation/grasp_eval.py index a783c7aa25..3f4b480590 100644 --- a/examples/manipulation/grasp_eval.py +++ b/examples/manipulation/grasp_eval.py @@ -5,8 +5,6 @@ from pathlib import Path import torch -import cv2 -import numpy as np try: try: @@ -16,9 +14,7 @@ if metadata.version("rsl-rl-lib") != "2.2.4": raise ImportError except (metadata.PackageNotFoundError, ImportError) as e: - raise ImportError( - "Please uninstall 'rsl_rl' and install 'rsl-rl-lib==2.2.4'." - ) from e + raise ImportError("Please uninstall 'rsl_rl' and install 'rsl-rl-lib==2.2.4'.") from e from rsl_rl.runners import OnPolicyRunner import genesis as gs @@ -32,9 +28,7 @@ def load_rl_policy(env, train_cfg, log_dir): runner = OnPolicyRunner(env, train_cfg, log_dir, device=gs.device) # Find the latest checkpoint - checkpoint_files = [ - f for f in log_dir.iterdir() if re.match(r"model_\d+\.pt", f.name) - ] + checkpoint_files = [f for f in log_dir.iterdir() if re.match(r"model_\d+\.pt", f.name)] if not checkpoint_files: raise FileNotFoundError(f"No checkpoint files found in {log_dir}") @@ -54,9 +48,7 @@ def load_bc_policy(env, bc_cfg, log_dir): bc_runner = BehaviorCloning(env, bc_cfg, None, device=gs.device) # Find the latest checkpoint - checkpoint_files = [ - f for f in log_dir.iterdir() if re.match(r"checkpoint_\d+\.pt", f.name) - ] + checkpoint_files = [f for f in log_dir.iterdir() if re.match(r"checkpoint_\d+\.pt", f.name)] if not checkpoint_files: raise FileNotFoundError(f"No checkpoint files found in {log_dir}") @@ -70,60 +62,6 @@ def load_bc_policy(env, bc_cfg, log_dir): return bc_runner._policy -def get_stereo_frame(env, step_count): - """Get stereo frame as numpy array.""" - # Get stacked stereo rgb image (B, 6, H, W) - stacked_stereo_rgb = env.get_stereo_rgb_images(normalize=False).cpu().numpy()[0] - stacked_stereo_rgb = stacked_stereo_rgb.transpose(1, 2, 0) - - # Split stacked stereo rgb image into left and right images - left_img, right_img = np.split(stacked_stereo_rgb, 2, axis=2) - # Convert RGB to BGR for OpenCV display - left_img_bgr = cv2.cvtColor(left_img, cv2.COLOR_RGB2BGR) - right_img_bgr = cv2.cvtColor(right_img, cv2.COLOR_RGB2BGR) - stereo_rgb_img = np.concatenate([left_img_bgr, right_img_bgr], axis=1) - - # Add a vertical line separator between the two images - separator_x = left_img.shape[1] - cv2.line( - img=stereo_rgb_img, - pt1=(separator_x, 0), - pt2=(separator_x, stereo_rgb_img.shape[0]), - color=(255, 255, 255), - thickness=2, - ) - return stereo_rgb_img - - -def save_frames_as_video(frames, video_path, fps=60): - """Save a list of frames as a video.""" - if not frames: - print("No frames to save!") - return - - # Get frame dimensions from the first frame - height, width = frames[0].shape[:2] - - # Create video writer - fourcc = cv2.VideoWriter_fourcc(*"mp4v") - video_writer = cv2.VideoWriter(video_path, fourcc, fps, (width, height)) - - if not video_writer.isOpened(): - print(f"Error: Could not open video writer for {video_path}") - return - - # Write frames to video - print(f"Saving {len(frames)} frames to {video_path}...") - for i, frame in enumerate(frames): - video_writer.write(frame) - if i % 30 == 0: # Progress indicator every 30 frames - print(f"Progress: {i + 1}/{len(frames)} frames") - - # Release video writer - video_writer.release() - print(f"Video saved successfully: {video_path}") - - def main(): parser = argparse.ArgumentParser() parser.add_argument("-e", "--exp_name", type=str, default="grasp") @@ -157,16 +95,11 @@ def main(): # Load configurations if args.stage == "rl": # For RL, load the standard configs - env_cfg, reward_cfg, robot_cfg, rl_train_cfg, bc_train_cfg = pickle.load( - open(log_dir / "cfgs.pkl", "rb") - ) + env_cfg, reward_cfg, robot_cfg, rl_train_cfg, bc_train_cfg = pickle.load(open(log_dir / "cfgs.pkl", "rb")) else: # For BC, we need to load the configs and create BC config - env_cfg, reward_cfg, robot_cfg, rl_train_cfg, bc_train_cfg = pickle.load( - open(log_dir / "cfgs.pkl", "rb") - ) + env_cfg, reward_cfg, robot_cfg, rl_train_cfg, bc_train_cfg = pickle.load(open(log_dir / "cfgs.pkl", "rb")) - env_cfg["visualize_camera"] = True # set the max FPS for visualization env_cfg["max_visualize_FPS"] = 60 # set the box collision @@ -175,6 +108,8 @@ def main(): env_cfg["box_fixed"] = False # set the number of envs for evaluation env_cfg["num_envs"] = 10 + # for video recording + env_cfg["visualize_camera"] = args.record env = GraspEnv( env_cfg=env_cfg, @@ -189,16 +124,17 @@ def main(): else: policy = load_bc_policy(env, bc_train_cfg, log_dir) policy.eval() - # Verify policy is float32 - print(f"Policy dtype: {next(policy.parameters()).dtype}") obs, _ = env.reset() - # Initialize frame list for recording - frames = [] max_sim_step = int(env_cfg["episode_length_s"] * env_cfg["max_visualize_FPS"]) with torch.no_grad(): + if args.record: + print("Recording video...") + env.vis_cam.start_recording() + env.left_cam.start_recording() + env.right_cam.start_recording() for step in range(max_sim_step): if args.stage == "rl": actions = policy(obs) @@ -211,22 +147,15 @@ def main(): # Collect frame for video recording if args.record: - frame = get_stereo_frame(env, step) - frames.append(frame) + env.vis_cam.render() # render the visualization camera obs, rews, dones, infos = env.step(actions) env.grasp_and_lift_demo() - - # Save video if recording was enabled - if args.record and frames: - video_path = log_dir / "stereo_evaluation.mp4" - # Save frames as video - fps = env_cfg["max_visualize_FPS"] - save_frames_as_video(frames, video_path, fps) - - # Close OpenCV windows if BC evaluation - if args.stage == "bc": - cv2.destroyAllWindows() + if args.record: + print("Stopping video recording...") + env.vis_cam.stop_recording(save_to_filename="video.mp4", fps=env_cfg["max_visualize_FPS"]) + env.left_cam.stop_recording(save_to_filename="left_cam.mp4", fps=env_cfg["max_visualize_FPS"]) + env.right_cam.stop_recording(save_to_filename="right_cam.mp4", fps=env_cfg["max_visualize_FPS"]) if __name__ == "__main__": @@ -235,11 +164,11 @@ def main(): """ # evaluation # For reinforcement learning model: -python examples/manipulation/grasp_eval.py --stage rl +python examples/manipulation/grasp_eval.py --stage=rl # For behavior cloning model: -python examples/manipulation/grasp_eval.py --stage bc +python examples/manipulation/grasp_eval.py --stage=bc # With video recording: -python examples/manipulation/grasp_eval.py --stage bc --record +python examples/manipulation/grasp_eval.py --stage=bc --record """