Using EmbodiChain with RLinf

RLinf is an optional external training backend for EmbodiChain environments. This page explains the boundary between the two stacks and the EmbodiChain contracts that an RLinf worker consumes. It does not repeat RLinf’s installation, launcher, or task recipe; use the official RLinf EmbodiChain guide for that workflow.

Choose a training stack

The two stacks serve different experiment lifecycles. Use this as a workflow guideline when deciding where to put the policy and training runtime:

Experiment profile	Recommended stack	Typical lifecycle
Locomotion or a simple visual policy	EmbodiChain RL (embodichain train-rl)	Configure the task, policy, algorithm, and trainer in EmbodiChain and usually train from scratch
A pretrained VLA or WAM policy	RLinf	Start from pretrained weights, run SFT when needed, then continue with RL using RLinf’s actor, rollout, and distributed training stack

This is a recommended workflow boundary rather than a hard capability restriction. EmbodiChain owns the Gym task, observations, actions, rewards, and simulation configuration. RLinf is useful when the experiment needs a pretrained model lifecycle or RLinf’s distributed training runtime.

For EmbodiChain’s native RL workflow, see the RL tutorial and the RL overview. For the external training workflow, see the official RLinf EmbodiChain guide.

EmbodiChain integration contract

An RLinf worker uses the same installed task package and runnable Gym deployment as the EmbodiChain CLI:

• The RLinf environment selects env_type: embodichain and points gym_config_path at a runnable deployment with a non-empty id.

• The task package must be installed in the Python environment used by RLinf workers and expose the embodichain.tasks entry point.

• Package initialization hooks must be safe to run more than once because workers construct environments independently.

• The deployment’s observation and action spaces remain the source of truth; the RLinf policy dimensions and mapping must match them.

See the task package registration contract for discovery and initialization details. See the configuration guide for inline and componentized Gym deployment paths.

The official RLinf recipe may move at a different pace from EmbodiChain. Older versions of that recipe use the retired configs/agents/rl/basic/<task>/gym_config.json layout. Current EmbodiChain deployments live under embodichain_tasks/configs/tasks/<category>/<task>/env.json or a corresponding task deployment selected by the project configuration conventions.

Current compatibility boundary

The adapter is intentionally conservative. Verify these boundaries before scaling a training run:

Capability	Current status
Inline env.json/env.yaml deployment	Supported first
Python @register_env task	Supported when its package is installed in the RLinf runtime
External manager or asset resolver	Supported through an embodichain.init hook
Componentized deployment	Experimental; verify relative component paths
Configuration-defined Task Program	Experimental; verify the adapter version
Default physics backend	Supported first
Newton backend and custom simulation settings	Verify that the adapter preserves EmbodiChain’s sim_cfg
Low-dimensional robot state	Supported through explicit adapter state mapping
Object, camera, language, or nested observations	Requires an explicit mapping
Flat continuous Box actions	Supported first
Dict, semantic, or VLA/chunked actions	Requires an action converter

Before a long run, verify a worker-local reset() and step() smoke test, the policy observation and action dimensions, the selected physics backend, and any reusable component paths. Task discoverability alone does not prove that observation semantics, action semantics, or simulation settings are ready for RLinf.

Ownership and support

Area	Owner
Task registration, Gym deployment, robot and scene configuration, managers, and physics settings	EmbodiChain
Actor, rollout, trainer, placement, checkpoints, and distributed execution	RLinf

Report task registration, configuration composition, robot or scene, manager, and physics issues to the EmbodiChain integration. Report actor, rollout, placement, checkpoint, and trainer issues to RLinf.