mjlab-homierl

KO | EN

gitlite — search

#javascript #hacktoberfest #ai #typescript #llm #machine-learning #deep-learning #nodejs #cli #kubernetes #vue #awesome-list #ios #open-source

(★ 31)

Fork of mjlab which supports unitree H1/G1 & robotiq_2f85, personal implementation of the locomotion policy training in "HOMIE: Humanoid Loco-Manipulation with Isomorphic Exoskeleton Cockpit [RSS2025]"

mjlab-homierl Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# mjlab-homierl

[Screencast from 2026-03-25 19-53-04.webm](https://github.com/user-attachments/assets/a401ea12-95c9-4aec-a6dd-0c6c99aa47aa)

`mjlab-homierl` is an external `mjlab` task package reproducing the lower-body
locomotion RL portion of
[HOMIE: Humanoid Loco-Manipulation with Isomorphic Exoskeleton Cockpit](https://arxiv.org/abs/2502.13013)
on Unitree G1 (the robot used by the original OpenHomie release) and Unitree H1.
It follows the standard `anymal_c_velocity` style layout: the repository only
contains custom tasks, assets, and the HIM-PPO training stack, while the core
simulator/runtime comes from upstream `mjlab`.

Reward terms, weights, command sampling (1/3 squat, 1/2 walk, 1/6 stand every
4 s), the upper-body disturbance curriculum, and the HIM-PPO hyperparameters
follow the OpenHomie reference implementation (`HomieRL/legged_gym`).
Intentional deviations from OpenHomie are:

- Contact-based termination is replaced by contact penalties plus a
  torso-contact termination (G1), matching MuJoCo Warp's contact model.
- Domain randomization uses mjlab-native `dr.*` events (PD gains, link mass,
  payload, CoM offset, encoder bias, foot friction). OpenHomie's per-step
  torque injection has no mjlab equivalent and is approximated by PD-gain
  randomization and encoder bias.
- Per-joint action scales are derived from actuator effort/stiffness
  (`0.25 * effort / stiffness`), the mjlab convention, instead of a uniform
  0.25.
- The HIM estimator's next-step critic observation at termination steps is the
  post-reset observation (mjlab computes observations after resets).

## Which task/checkpoint is the HOMIE reproduction?

- **Task**: `Mjlab-Homie-Unitree-G1` (deployment-grade PD gains, sim2real
  path). `Mjlab-Homie-Unitree-G1-native` is the frozen strict-parity preset —
  the diff between the two configs is the complete, auditable ledger of our
  deliberate deviations.
- **Pretrained checkpoints**: [Hugging Face — Nagi-ovo/HOMIERL-loco](https://huggingface.co/Nagi-ovo/HOMIERL-loco).
- **Height convention**: our height command is pelvis-to-**sole** (standing
  0.78 m, range 0.28–0.78). OpenHomie's numbers are pelvis-to-**ankle**
  (standing 0.74, range 0.24–0.74). Same physical pose and identical 0.50 m
  travel — subtract ~0.04 m when comparing to OpenHomie figures.
- Deployment-oriented research forks (e.g. commanded torso pitch) are
  developed on separate branches and intentionally kept off `main` so this
  branch stays a faithful reproduction.

## Structure

```text
src/mjlab_homierl/
  __init__.py              # entry-point task registration
  homie_env_cfg.py         # base HOMIE task config (OpenHomie rewards/commands)
  env_cfgs.py              # G1 / H1 / H1-with-hands configs
  rl_cfg.py                # HIM-PPO runner config
  mdp/                     # HOMIE commands, rewards, observations, actions
  rl/                      # HIM-PPO algorithm, runner, ONNX export
  robots/
    unitree_h1/            # H1 XML + constants (G1 comes from mjlab's asset zoo)
    robotiq_2f85/          # gripper XML assets
```

## Install

Clone the repository first:

```bash
git clone https://github.com/Nagi-ovo/mjlab-homierl.git
cd mjlab-homierl
```

For GPU training and playback:

```bash
uv sync --extra cu128
```

For CPU-only setups:

```bash
uv sync --extra cpu
```

For local docs builds:

```bash
uv sync --extra docs
```

This package depends on upstream `mjlab>=1.5.0,<1.6.0` and registers tasks
through the `mjlab.tasks` entry-point group.

## Registered Tasks

- `Mjlab-Homie-Unitree-G1` — trains with deployment-grade PD gains (the table
  HomieDeploy's real-robot low-level controller runs; see
  `robots/unitree_g1_deploy.py`) and the deployed pipeline's uniform 0.25
  action scale. Use this for sim2real. The effective per-joint
  stiffness/damping is embedded in the exported ONNX metadata, so deployment
  code can read the gains from the policy file. Waist defaults to `locked`:
  waist_roll/pitch are PD-held at the default pose and excluded from the
  upper-body disturbance, matching OpenHomie's 27-dof G1 (its URDF welds those
  two joints) and real-robot deployment, which software-holds them.

All G1 variants below share the base task's observation/action interface, so
checkpoints load interchangeably across them (and into the base task):

- `Mjlab-Homie-Unitree-G1-free_waist` — all three waist joints join the random
  upper-body disturbance; a strict superset of the original training
  distribution (the torso can be randomly pitched/rolled).
- `Mjlab-Homie-Unitree-G1-with_dex3` — Unitree Dex3 hands mounted as inertial
  attachments (~0.53 kg each) plus a randomized held-object payload.
- `Mjlab-Homie-Unitree-G1-with_inspire` — Inspire RH56 hands (RH56DFX spec
  mass, 0.54 kg each), same treatment. The base task randomizes a wrist
  payload covering these hand masses, so one checkpoint serves bare wrists
  and both hand models; these variants are primarily for play/eval with the
  real hand geometry.
- `Mjlab-Homie-Unitree-G1-mjlab_gains` — ablation variant with mjlab's
  first-principles actuator gains (armature × natural frequency); sim-only.
- `Mjlab-Homie-Unitree-G1-native` — frozen OpenHomie-parity preset. The diff
  between this cfg and the default task is the authoritative ledger of our
  deliberate deviations (torso payload DR narrowed to (-1, +5) kg, wrist
  payload DR widened to (0, 1.5) kg, hip/knee ground-contact penalty added —
  OpenHomie's `penalize_contacts_on` is dead code). Reference baseline; never
  iterated on.

- `Mjlab-Homie-Unitree-H1` — trains with Unitree's official RL-stack PD gains
  (unitree_rl_gym `h1_config.py`; see `robots/unitree_h1_deploy.py`) and the
  uniform 0.25 action scale.
- `Mjlab-Homie-Unitree-H1-mjlab_gains` — H1 ablation variant with
  first-principles gains; sim-only.
- `Mjlab-Homie-Unitree-H1-with_hands` — deploy gains, with Robotiq 2F85
  grippers mounted.

## Usage

Pretrained locomotion checkpoints are available at
[Hugging Face](https://huggingface.co/Nagi-ovo/HOMIERL-loco) if you want to skip
training and go straight to playback.

List available environments:

```bash
uv run list-envs
```

Train:

```bash
uv run train Mjlab-Homie-Unitree-G1 --env.scene.num-envs 4096
uv run train Mjlab-Homie-Unitree-H1 --env.scene.num-envs 4096
```

The runner config sets `upload_model=False`: metrics are logged to W&B (when
the `wandb` logger is selected) but checkpoints and ONNX exports stay local.
Use `--agent.logger tensorboard` to skip W&B entirely.

Note: the HIM-PPO algorithm is single-GPU; the upstream `--gpu-ids` multi-GPU
path is not supported.

Play:

```bash
uv run play Mjlab-Homie-Unitree-G1 --checkpoint-file /path/to/model.pt --viewer viser
```

More explicit playback examples:

```bash
uv run play Mjlab-Homie-Unitree-G1 \
  --checkpoint-file /path/to/model.pt \
  --num-envs 30 \
  --viewer viser \
  --device cuda:0
```

Sanity-check the MDP before training:

```bash
uv run play Mjlab-Homie-Unitree-G1 --agent zero
uv run play Mjlab-Homie-Unitree-G1 --agent random
```

Play holds the upper body at its default pose by default (isolating the
lower-body gait). To preview deployment-like upper-body disturbances instead:

```bash
HOMIE_PLAY_UPPER_RATIO=1.0 uv run play Mjlab-Homie-Unitree-G1 --checkpoint-file ...
```

## Real-robot deployment (G1)

`src/mjlab_homierl/scripts/deploy_g1_homie.py` runs an exported policy ONNX on
the real robot over DDS, reading every convention (joint order, PD gains,
obs layout, command ranges) from the ONNX metadata. The real path needs only `numpy + onnxruntime + unitree_sdk2py` —
no mjlab/torch on the robot-side machine. One-time environment setup (builds
cyclonedds 0.10.2 and works around three upstream packaging defects — see the
script header):

```bash
bash scripts/setup_deploy_env.sh
```

Then (robot in debug/low-level mode, harnessed):

```bash
.venv-deploy/bin/python src/mjlab_homierl/scripts/deploy_g1_homie.py \
  --onnx <run_dir>/<run>.onnx --net <iface>
```

START moves to the default pose, A starts the policy, sticks drive vx/vy/wz,
dpad up/down slews the height command, SELECT exits to damping. `--sim` (in the training venv:
`uv run --extra deploy ... --sim`) validates the deploy-side observation
builder bit-for-bit against the mjlab plant before any hardware session.

## Interactive sim teleop (keyboard, classic MuJoCo)

```bash
uv run python -m mjlab_homierl.scripts.teleop_sim_g1 --onnx <run>.onnx
```

Real-time keyboard teleop in a plain CPU MuJoCo window, driving the policy
through the same `runtime.py` pipeline as the real robot and the BiGym
plugin — physics independent of the training engine (sim2sim). WASD = vx/vy,
Q/E = yaw, arrows = height, Space = stop,
Backspace = reset; Ctrl+drag shoves the robot. `--smoke` runs a 5 s headless
self-test. Note for anyone building a classic-MuJoCo harness from the raw
asset-zoo spec: it compiles with NO actuators, NO keyframe, and zero
armature (mjlab injects all three at Entity build time), and the default
Euler integrator is unstable at knee kp = 300 — this script re-injects all
four (see `build_model`).

## Notes

- The repository no longer vendors the `mjlab` framework itself.
- HIM-PPO remains package-local under `mjlab_homierl.rl.himppo`. Left/right
  mirror maps for its symmetry augmentation are derived from joint names, so
  any humanoid with `left_*`/`right_*` joint naming works.
- `Mjlab-Homie-Unitree-H1-with_hands` mounts the 2F85 grippers with hand
  collisions disabled by default. For HOMIE this keeps the hands as inertial /
  disturbance attachments instead of contact-rich manipulation tools.
- `uv run play ...` uses a HOMIE-specific actor-only inference path. The play
  env strips critic observations, rewards, and curriculum to reduce playback
  overhead.
- The custom inference helper is exposed as:

```bash
uv run infer-homie-lowerbody --help
```

## Development

Run the package regression tests:

```bash
uv run pytest tests -q
```

Build the docs locally:

```bash
uv run --extra docs sphinx-build docs docs/_build
```

mjlab-homierl

mjlab-homierl Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

mjlab-homierl

mjlab-homierl Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README