| name | add-embodiment-component |
| description | Add or update a reusable EmbodiChain embodiment YAML component that owns one simulation robot, its sensor suite, and an optional Task Program skill_profile. Use for configs/components/embodiments, new Task Program-compatible robots, or multi-embodiment task deployment. |
Add Embodiment Component
Create the reusable physical and semantic declaration that lets one robot
configuration serve many tasks.
Establish scope
An embodiment component belongs at:
embodichain_tasks/configs/components/embodiments/<embodiment_id>.yaml
It owns exactly:
embodiment_id;
- one
simulation robot mapping;
- the complete
sensor list;
- optional
skill_profile.
It does not own a task, physical scene, Task Program, execution policy,
task-local object IDs, or runnable Gym ID. Component files do not use a
compatibility version field.
Load current context
Read agent_context/MAP.yaml, then resolve:
robot-system;
sensor-system; and
task-programs when a skill_profile is requested.
Inspect the closest current component under
embodichain_tasks/configs/components/embodiments/ and the intended runnable
task deployment.
Reuse or add the robot
If an existing registered robot config can express the request, select it in
simulation and add only necessary config overrides. If a new
RobotCfg/URDF/control-part implementation is required, invoke $add-robot
first. This skill does not duplicate robot registration logic in YAML.
Keep the sensor suite atomic with the embodiment. Sensor parent link names must
exist on the selected robot.
Add skill_profile only when needed
Handwritten and RL deployments may use an embodiment without semantic
metadata. A configured Task Program requires skill_profile with:
contract_id: the compatibility contract tasks/policies require;
profile_id: stable unversioned profile identity;
resources: logical task-facing resources;
command_presets: named control commands;
- optional
runtime_services.
Each resource declares logical endpoints. Endpoint declarations map to actual
control_part names and list only capabilities the selected robot/runtime
can provide. Grasp endpoints that require open/grasp commands select a matching
command preset.
Use logical resource IDs such as primary_manipulator, left, or right;
Task Program integrations bind Semantic Call slots to these IDs. Do not expose
task-local names as reusable resources.
Runtime service ownership
Put embodiment-calibrated services in skill_profile.runtime_services, such
as gripper-specific antipodal grasp generation or control-part evidence. Put
task-specific targets, poses, and overrides in the task integration.
For reusable joint-position constraint evidence, omit object_ids unless the
embodiment truly supports only an explicit global set. Omission lets runtime
assembly scope evidence to graspable objects in the selected task scene.
Only use service kinds accepted by the current configured decoder. Never
serialize a dotted import or callable.
Attach without copying
A runnable deployment selects:
embodiment:
component: ../../../components/embodiments/<embodiment_id>.yaml
Optional deployment overrides are limited to uid, init_pos, init_rot,
and init_qpos. Do not repeat inline robot or sensor fields.
One task can provide multiple thin deployments selecting compatible
embodiments. Keep the program embodiment-independent and validate the
integration and policy contracts against every variant.
Validation
At minimum:
- decode the YAML as a closed embodiment component;
- build the selected robot config and verify control parts;
- verify sensor parent links and shapes;
- verify every profile endpoint/control part/capability and command preset;
- compose at least one intended task deployment;
- for Task Program use, run
$add-task-program's deployment inspector;
- run a minimal live robot/sensor smoke test when available.
Checklist