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name: rex-gym description: "Operate the legacy Rex-Gym quadruped reinforcement-learning package for PyBullet environments, kinematics and gait modeling, PPO training setup, and packaged policy playback." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Rex-Gym operating skill

Use this skill when a task involves the rex_gym package, Rex/SpotMicro quadruped simulation, PyBullet Gym environments, inverse kinematics, Bezier walking/galloping, motor models, legacy PPO training, or shipped policy checkpoints.

Route the request

• Use simulation-environments for environment construction, task/signal/terrain/mark selection, reset/step, observations, rewards, termination, and headless or GUI simulation.
• Use locomotion-modeling for Kinematics, GaitPlanner, pose/frame ordering, motor constants, and torque conversion.
• Use training-policy for rex-gym train, rex-gym policy, PPO configuration, agent counts, log directories, policy ids, checkpoints, and TensorFlow compatibility.

Read troubleshooting first when installation, legacy dependencies, package data, or a runtime error is involved. Read repository provenance before deciding whether this graph matches a changed checkout. Run the shared package inspector for a no-training import, CLI, mapping, and deterministic-kinematics check; use troubleshooting for cross-cutting failures.

Install and establish the compatibility family

The public distribution is rex_gym, imported as rex_gym, and exposes the rex-gym console command:

python -m pip install rex_gym
python -c "import rex_gym; print('rex_gym import ok')"
rex-gym --help

For the pinned repository behavior, prefer an isolated Python 3.7 environment with NumPy 1.17.3, PyBullet 2.8.3, Gym 0.17.1, TensorFlow 1.15.5, TensorFlow Probability 0.8, ruamel.yaml, and Click. Modern pip reports an upstream metadata conflict between Gym 0.17.1's cloudpickle range and TFP 0.8's exact pin; do not casually upgrade this legacy family. TensorFlow 1.15 also needs a protobuf 3.20.x-or-earlier runtime on modern installations. See the training route for the optional PPO surface and the troubleshooting reference for recovery.

Choose a safe execution mode

Prefer headless PyBullet (render=False) and a bounded step count on servers, CI, and first checks. Use the simulation smoke helper for a deterministic package/API check; it supplies terrain_id explicitly and always closes the environment. Do not start full training or policy playback merely to test an import. GUI playback and playground training require a display and can run until the environment ends.

The legacy environment API is reset() followed by step(action) returning (observation, reward, done, info), not the newer terminated/truncated pair. Use task-specific action spaces, and expect info["action"] to contain the transformed motor command. Always close environments in finally blocks.

Scope and non-goals

This graph covers the public simulation and learning tools in Rex-Gym. It does not operate physical hardware, replace rexctl, validate sim-to-real transfer, claim policy quality, or prescribe long PPO experiments without an explicit bounded run plan. Large TensorFlow checkpoint binaries remain package data; inspect their catalog and sidecars rather than copying them into this skill.

The source package is legacy and has no dedicated repository test suite or examples tree. The bundled routes and scripts therefore combine documented workflows, source-backed API facts, live import checks, and bounded synthetic smokes. Use the verification artifacts only as construction evidence; they are not runtime dependencies.