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Plugins & the registry

Inspect Robots components register by name and resolve from strings: the mechanism the CLI and eval("...", "...", "...") use. In-tree builtins register via decorators; out-of-tree packages publish entry points, so an installed plugin appears in inspect-robots list without being imported first.

Decorators​

from inspect_robots.registry import embodiment, policy, scorer, task

@policy("my-vla")
class MyVLA: ...

@embodiment("my-arm")
class MyArm: ...

@scorer("smooth")
def smooth(): ...

@task("my-bench")
def my_bench(): ...

Resolving​

from inspect_robots.registry import registered, resolve

registered("policy")          # {"scripted": ..., "random": ..., "my-vla": ...}
policy = resolve("policy", "my-vla", checkpoint="...")   # constructor kwargs forwarded

Shipping an out-of-tree plugin​

Publish entry points from your package's pyproject.toml:

[project.entry-points."inspect_robots.embodiments"]
maniskill = "inspect_robots_maniskill:ManiSkillEmbodiment"

[project.entry-points."inspect_robots.policies"]
openvla = "inspect_robots_openvla:OpenVLAPolicy"

Groups: inspect_robots.tasks, inspect_robots.policies, inspect_robots.embodiments, inspect_robots.scorers, inspect_robots.sinks. After pip install inspect-robots-maniskill, it shows up in inspect-robots list and resolves by name in eval() and the CLI.

This is how the ecosystem stays decoupled: this repository is the framework; specific simulators, VLA weights, and benchmarks live in their own packages.

Reading user defaults​

Plugin CLIs can read the configuration written by inspect-robots setup through inspect_robots.defaults. Constructor args belong to the component named by their owner field — apply embodiment_args only when the owner is the embodiment your plugin drives, or args recorded for another rig will configure yours:

import os

from inspect_robots.defaults import load_defaults

defaults = load_defaults(os.environ)
if defaults.embodiment_args_owner == "my_embodiment":
    args = defaults.embodiment_args

A plugin that ships several embodiments (or expects subclasses registered by other packages) can resolve the owner with inspect_robots.registry.registered("embodiment") and check the factory's class instead of comparing names.

First-party plugins​

Five adapters ship from the Inspect Robots repository as separate packages, covering both halves of an eval:

  • inspect-robots-ros: run evals on ROS 1 or ROS 2 arms through rosbridge, with no ROS installation on the eval machine (--embodiment ros).
  • inspect-robots-isaacsim: run evals against an Isaac Lab simulation (--embodiment isaacsim).
  • inspect-robots-xpolicylab: drive any XPolicyLab-served policy. One adapter puts its zoo of 40+ VLAs (π0/π0.5, GR00T, OpenVLA-OFT, RDT-1B, SmolVLA, ACT, …) behind --policy xpolicylab -P url=ws://gpu-box:19000.
  • inspect-robots-agent: let a frontier LLM (Claude, GPT, or anything behind an OpenAI-compatible API) drive any embodiment through tool calls as a first-class policy. The same --policy agent runs ad-hoc instructions and scores on registered tasks next to fine-tuned VLAs.
  • inspect-robots-capx: evaluate CaP-X-style code-as-policy agents against a joint-space embodiment. Model-generated Python calls separately served SAM3, Contact-GraspNet, and Pyroki helpers, then queues approver-checked joint targets behind --policy capx.

inspect-robots-isaacsim: the body​

Wraps an Isaac Lab simulation as an embodiment. Installing it makes isaacsim resolvable; only reset()/step() need a working Isaac install (listing and compatibility checks run anywhere):

pip install inspect-robots-isaacsim
inspect-robots run --task my-task --policy my-vla --embodiment isaacsim \
    -E task_id=Isaac-Lift-Cube-Franka-v0

inspect-robots-xpolicylab: the brain​

Drives any XPolicyLab-served policy. XPolicyLab wraps 40+ VLA / imitation-learning policies (π0/π0.5, GR00T, OpenVLA-OFT, RDT-1B, SmolVLA, ACT, …) behind one websocket policy-server contract; this adapter speaks that protocol directly, so the whole zoo becomes evaluable without installing any model dependencies locally:

pip install inspect-robots-xpolicylab

# terminal 1 — serve a policy from an XPolicyLab checkout (its own env/machine)
cd XPolicyLab/policy/Pi_0 && bash setup_eval_policy_server.sh ... 19000 0.0.0.0

# terminal 2 — evaluate it
inspect-robots run --task my-task --policy xpolicylab --embodiment isaacsim \
    -P url=ws://gpu-box:19000 -P cameras=cam_head:base_rgb

See each plugin's linked README for its full configuration reference.