Scaffold EmbodiChain task environments with Gym IDs and test stubs.
npx skills add https://github.com/DexForce/EmbodiChain --skill add-task-env
Please help me install this Agent Skill. Skill: add-task-env Source: https://github.com/DexForce/EmbodiChain/tree/main/skills/add-task-env Command: npx skills add https://github.com/DexForce/EmbodiChain --skill add-task-env
This Skill helps you quickly add a new task environment to EmbodiChain without manually figuring out the required file layout, registration hooks, and testing scaffolds.
tableware, rl, special).@register_env decorator usage, and extension points for expert demos (create_demo_action_list) or RL tasks (get_reward, compute_task_state, check_truncated).tasks/__init__.py exports so the new environment is discoverable by the framework.black to match repository conventions.Use the add-task-env skill to scaffold a new task environment named pick_place for the category rl with Gym ID PickPlace-v1, then add it to tasks/init.py and create the corresponding tests stub.
High-intent search queries and answers about installing and using this skill.
Scaffolding a new gym environment requires selecting a task category and snake_case name, then generating a Python module with reward and task state extension points using the EmbodiedEnv conventions. The environment is registered with a unique Gym ID and exported in tasks/__init__.py.
Registering a unique Gym ID involves applying the @register_env decorator within the new task Python module. The module must follow the EmbodiedEnvCfg conventions and be exported through tasks/__init__.py to become discoverable by the EmbodiChain framework.
Yes, creating expert demonstration tasks is supported by defining the create_demo_action_list extension point within the scaffolded Python module. This follows the EmbodiedEnv conventions alongside RL task methods like get_reward and compute_task_state.
Available task categories map directly to EmbodiChain's folder structure: tableware, rl, and special. Selecting the correct category determines where the Python module is created within the tasks directory hierarchy.
Yes, adding a test stub is required to pass the quality gate and match repository conventions. The scaffolding process also runs formatting with black to ensure the new environment module adheres to the project's code style standards.
Environments become discoverable only when properly exported through tasks/__init__.py. If the @register_env decorator is missing or the unique Gym ID is not registered within the Python module, the EmbodiChain framework cannot locate the new task.
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