Embodiment Design

Embodiments define robot-specific configurations and behaviors. They provide a modular way to integrate different robots into environments, encapsulating kinematics, control actions, observations, and camera systems.

Core Architecture

Embodiments use the EmbodimentBase abstract class that extends the asset system:

class EmbodimentBase(Asset):
    name: str | None = None
    tags: list[str] = ["embodiment"]

    def __init__(self, enable_cameras: bool = False, initial_pose: Pose | None = None):
        self.scene_config: Any | None = None
        self.action_config: Any | None = None
        self.observation_config: Any | None = None
        # ... other configs

    @abstractmethod
    def get_scene_cfg(self) -> Any:
        """Robot physical configuration and actuators."""

    @abstractmethod
    def get_action_cfg(self) -> Any:
        """Control interface definition."""

Embodiments in Detail

Configuration Components

Embodiments contribute multiple configuration aspects to environments:

• Scene Configuration: Robot physical properties, actuators, initial pose, mounting stands

• Action Configuration: Control interface (joint positions, end-effector poses, WBC)

• Observation Configuration: Sensor data, joint states, end-effector poses, camera feeds

• Event Configuration: Robot initialization, resets, randomization

• Camera Configuration: Onboard cameras with positions and sensor properties (optional)

• XR Configuration: XR device locations for teleop integration (optional)

• Mimic Configuration: Mimic environment support for demonstration (optional)

Available Embodiments

Robot assets with different capabilities and control modes:

• Franka Panda: 7-DOF manipulator with differential IK control

• Unitree G1: Humanoid with WBC, dual-arm manipulation, locomotion

• GR1T2: Humanoid optimized for manipulation with dual end-effector control

• Control Variants: Joint space vs. inverse kinematics control modes

Camera Integration

Optional camera systems that add observation terms when enabled, supporting both manipulation and perception tasks with head-mounted or external cameras.

Environment Integration

# Embodiment creation with camera support
embodiment = asset_registry.get_asset_by_name("franka")(
    enable_cameras=True
)

# Set robot initial pose
embodiment.set_initial_pose(
    Pose(position_xyz=(0.0, 0.0, 0.0), rotation_wxyz=(1.0, 0.0, 0.0, 0.0))
)

# Environment composition
environment = IsaacLabArenaEnvironment(
    name="manipulation_task",
    embodiment=embodiment,  # Robot configuration
    scene=scene,
    task=task,
    teleop_device=teleop_device
)

Usage Examples

Manipulation Robot

franka = asset_registry.get_asset_by_name("franka")(enable_cameras=True)
task = PickAndPlaceTask(pick_object, destination, background)

Humanoid Control Modes

# Joint space control
g1_joint = asset_registry.get_asset_by_name("g1_wbc_joint")()

# Inverse kinematics control
g1_ik = asset_registry.get_asset_by_name("g1_wbc_pink")()

Teleop Integration

embodiment = asset_registry.get_asset_by_name("gr1_pink")()
teleop_device = device_registry.get_device_by_name("avp_handtracking")()

environment = IsaacLabArenaEnvironment(
    embodiment=embodiment,
    teleop_device=teleop_device,
    scene=scene,
    task=task
)