humanoid_open_starter.py

#!/usr/bin/env python3
"""
humanoid_open_starter.py
========================
A single-file starter that combines open-source patterns for the three core
areas of humanoid robotics development:

1. Movement & Control   – Gymnasium / MuJoCo Humanoid-v4 style loop
2. AI / Policy          – simple random + optional Stable-Baselines3 PPO stub
3. Sensor / Observation – observation processing & basic vision-ready hooks

This file is intentionally self-contained and educational.
It is NOT a full production stack.  For real work use the projects surveyed:

  • LeRobot + GR00T N1.7   (end-to-end VLA + hardware abstraction)
  • humanoid-gym           (Isaac Gym RL + zero-shot sim2real)
  • HoloMotion / ProtoMotions / PHC  (whole-body foundation control)
  • Pinocchio + Pink       (kinematics / IK)
  • Berkeley Humanoid Lite / ToddlerBot  (low-cost full stacks)

Install minimal dependencies:
    pip install gymnasium[mujoco] numpy

Optional (for the PPO path):
    pip install stable-baselines3

Run:
    python humanoid_open_starter.py
"""

from __future__ import annotations

import time
from typing import Optional, Tuple

import numpy as np

# ---------------------------------------------------------------------------
# 1. Movement & Control  (Gymnasium Humanoid-v4 pattern)
# ---------------------------------------------------------------------------
try:
    import gymnasium as gym
except ImportError:
    raise ImportError(
        "Please install gymnasium with MuJoCo support:\n"
        "  pip install gymnasium[mujoco]"
    )


def make_humanoid_env(render: bool = True) -> gym.Env:
    """Create a standard Humanoid-v4 environment (open Gymnasium / MuJoCo)."""
    render_mode = "human" if render else None
    env = gym.make("Humanoid-v4", render_mode=render_mode)
    return env


# ---------------------------------------------------------------------------
# 2. Sensor / Observation handling
# ---------------------------------------------------------------------------
def process_observation(obs: np.ndarray) -> dict:
    """
    Minimal observation processing that mirrors the structure used by
    LeRobot, humanoid-gym and most modern humanoid stacks.

    In a real system this would also include:
      - camera images (OpenCV / ZMQ streams)
      - proprioception (joint positions / velocities)
      - IMU / force-torque
      - language instruction embedding (for VLAs like GR00T)
    """
    # Humanoid-v4 observation is a flat vector (~376–378 dims).
    # We keep it simple and just return a dict that future code can extend.
    return {
        "proprio": obs.astype(np.float32),
        "timestamp": time.time(),
        # Placeholder for future vision / language
        "image": None,
        "language": None,
    }


def mock_camera_frame(width: int = 64, height: int = 64) -> np.ndarray:
    """Tiny placeholder that shows where an OpenCV / ZMQ camera would plug in."""
    # In real code replace with:
    #   import cv2
    #   ret, frame = cap.read()
    return np.zeros((height, width, 3), dtype=np.uint8)


# ---------------------------------------------------------------------------
# 3. AI / Policy
# ---------------------------------------------------------------------------
class RandomPolicy:
    """Baseline open-loop random policy (useful for debugging the control loop)."""

    def __init__(self, action_space: gym.spaces.Box):
        self.action_space = action_space

    def select_action(self, obs_dict: dict) -> np.ndarray:
        return self.action_space.sample()


def try_load_ppo(env: gym.Env):
    """
    Optional: load a Stable-Baselines3 PPO agent if the package is present.
    This mirrors the training style used in many humanoid-gym examples.
    """
    try:
        from stable_baselines3 import PPO
        from stable_baselines3.common.vec_env import DummyVecEnv

        # For a real run you would train first:
        #   model = PPO("MlpPolicy", env, verbose=1)
        #   model.learn(total_timesteps=100_000)
        #   model.save("humanoid_ppo")
        #
        # Here we just create an untrained agent so the API is visible.
        vec_env = DummyVecEnv([lambda: env])
        model = PPO("MlpPolicy", vec_env, verbose=0)
        print("[AI] Stable-Baselines3 PPO available – using untrained agent for demo.")
        return model
    except ImportError:
        print("[AI] stable-baselines3 not installed – falling back to RandomPolicy.")
        return None


# ---------------------------------------------------------------------------
# Main control loop that ties the three areas together
# ---------------------------------------------------------------------------
def run_humanoid_loop(
    max_steps: int = 500,
    render: bool = True,
    use_ppo_if_available: bool = True,
):
    env = make_humanoid_env(render=render)
    obs, info = env.reset()

    # Policy selection
    ppo_model = try_load_ppo(env) if use_ppo_if_available else None
    random_policy = RandomPolicy(env.action_space)

    print("\n=== Humanoid Open Starter ===")
    print("Combining: Movement/Control  +  AI/Policy  +  Sensor/Observation")
    print("Press Ctrl+C to stop early.\n")

    total_reward = 0.0
    try:
        for step in range(max_steps):
            # ---- Sensor / Observation side ----
            obs_dict = process_observation(obs)
            # (Optional) attach a mock camera frame
            # obs_dict["image"] = mock_camera_frame()

            # ---- AI / Policy side ----
            if ppo_model is not None:
                # Stable-Baselines3 style
                action, _ = ppo_model.predict(obs, deterministic=False)
            else:
                action = random_policy.select_action(obs_dict)

            # ---- Movement & Control side ----
            obs, reward, terminated, truncated, info = env.step(action)
            total_reward += reward

            if step % 50 == 0:
                print(f"Step {step:4d} | reward={reward:7.2f} | total={total_reward:8.1f}")

            if terminated or truncated:
                print(f"Episode ended at step {step} (terminated={terminated}, truncated={truncated})")
                obs, info = env.reset()
                total_reward = 0.0

            if render:
                # Small sleep so the viewer is watchable
                time.sleep(0.01)

    except KeyboardInterrupt:
        print("\nInterrupted by user.")
    finally:
        env.close()
        print("Environment closed. Done.")


# ---------------------------------------------------------------------------
# Entry point
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    # Quick start – change these flags as needed
    run_humanoid_loop(
        max_steps=1000,
        render=True,                 # set False for headless / faster runs
        use_ppo_if_available=True,   # tries Stable-Baselines3 if installed
    )

    print(
        "\nNext steps (from the open-source survey):\n"
        "  1. Replace the policy with a LeRobot / GR00T N1.7 checkpoint\n"
        "  2. Move the loop into Isaac Lab or humanoid-gym for parallel training\n"
        "  3. Add real camera streams (OpenCV / ZMQ) and proprioception\n"
        "  4. Deploy on Unitree G1 / Berkeley Humanoid Lite / ToddlerBot\n"
    )