robots.md

Robots

RoboLab uses IsaacLab's ArticulationCfg to define robots. For details, refer to IsaacLab's documentation on robots. The robot config is passed as robot_cfg during RoboLab's environment registration.

Built-in Robot Configurations

Config	File	USD Asset	Gripper	Notes
DroidCfg	robolab/robots/droid.py	Franka + Robotiq 2F-85	Robotiq binary	High PD gains (400/80), wrist camera attached, gravity disabled
FrankaCfg	robolab/robots/franka.py	Franka Panda	Panda fingers	Standard PD gains (80/4), frame transformers for EE
FrankaCfg (high PD)	robolab/robots/franka_high_pd.py	Franka Panda	Panda fingers	High PD gains (400/80), gravity disabled

Each robot file also defines:

• Action configs — Joint position, IK, or relative IK action spaces
• Proprioception observations — Joint positions, gripper state, EE pose
• Contact gripper — Prim paths for contact detection on gripper fingers

Using a Built-in Robot

Import the robot config and pass it as robot_cfg in your registration function (see Environment Registration for the full example):

from robolab.robots.droid import DroidCfg, DroidJointPositionActionCfg, contact_gripper

# Inside your register_envs() function:
auto_discover_and_create_cfgs(
    robot_cfg=DroidCfg,
    actions_cfg=DroidJointPositionActionCfg(),
    contact_gripper=contact_gripper,
    # ... other registration kwargs
)

Defining a Custom Robot

Note

Creating a new robot in RoboLab is exactly the same as creating one in IsaacLab. You can bring over any robot configuration from IsaacLab (including all built-in configs and custom assets you've defined for IsaacLab), or create a new ArticulationCfg/@configclass robot from scratch by following the IsaacLab asset configuration and robot configuration tutorials.

There are no RoboLab-specific requirements for robot definition beyond having a robot field of type ArticulationCfg inside a configclass.

If it works in IsaacLab, it will work with RoboLab (plus one small addition)!

A robot config for RoboLab is a @configclass with a robot field (an ArticulationCfg) and optionally sensor fields (e.g., cameras). It can live in your own repository — there is no requirement to add it to the RoboLab package.

IsaacLab ships USD assets and pre-built configurations for many robots. You can use any of these. For how to write an ArticulationCfg (spawn settings, initial state, actuators, etc.), refer to IsaacLab's documentation:

• Writing an Asset Configuration — How to define ArticulationCfg with USD assets, rigid body properties, and actuators
• Interacting with an Articulation — Spawning and controlling articulated robots in simulation
• Robot Configuration in IsaacLab — End-to-end tutorial for configuring a new robot

The RoboLab-specific wrapper is a @configclass that exposes the ArticulationCfg as a robot field:

# my_repo/my_robot.py

from isaaclab.utils import configclass
from isaaclab.assets import ArticulationCfg


@configclass
class MyRobotCfg:
    robot = ArticulationCfg(
        # See IsaacLab docs for full ArticulationCfg reference:
        # spawn, init_state, actuators, rigid_props, articulation_props, etc.
        ...
    )

The field must be named robot and use prim_path="{ENV_REGEX_NS}/robot" for multi-env compatibility.

Adding a Wrist Camera to Your Robot

Robot-attached cameras (e.g., wrist cameras) are defined as fields on the robot config. The camera's prim_path must be under the robot's USD hierarchy — see Cameras for details.

from isaaclab.sensors import TiledCameraCfg

@configclass
class MyRobotCfg:
    robot = ArticulationCfg(...)

    wrist_cam = TiledCameraCfg(
        prim_path="{ENV_REGEX_NS}/robot/ee_link/wrist_cam",
        height=720, width=1280,
        data_types=["rgb"],
        spawn=sim_utils.PinholeCameraCfg(
            focal_length=2.8,
            focus_distance=28.0,
            horizontal_aperture=5.376,
            vertical_aperture=3.024,
        ),
        offset=TiledCameraCfg.OffsetCfg(
            pos=(0.01, -0.03, -0.07),
            rot=(-0.42, 0.57, 0.58, -0.41),
            convention="opengl",
        ),
    )

Defining Actions and Proprioception

You also need to define an action config and proprioception observations that match your robot's joints. See the built-in examples:

• Joint position actions: DroidJointPositionActionCfg in robolab/robots/droid.py
• IK actions: FrankaIKActionCfg / FrankaRelIKActionCfg in robolab/robots/franka_definitions.py
• Proprioception: ProprioceptionObservationCfg in robolab/robots/droid.py

Contact Gripper

For RoboLab, you must define the gripper contact prim paths. This highlights which grippers are "in contact" with an object.

contact_gripper = {"gripper": "{ENV_REGEX_NS}/robot/my_gripper/.*finger"}

Pass this as contact_gripper=contact_gripper in your registration kwargs.

See Also

• Cameras — Camera placement (scene cameras and robot-attached)
• Environment Registration — Wiring robot, cameras, observations, and actions into environments