OneHBC: A Unified Framework for Humanoid Body Control

OneHBC

A Unified Framework for Humanoid Body Control Training Based on IsaacLab Newton Physics Engine

OneHBC is a dedicated research framework for training humanoid robot locomotion and whole-body control policies using NVIDIA IsaacLab with the Newton physics engine. It supports high-performance end-to-end reinforcement learning and motion imitation for humanoid robots, with a focus on speed tracking, AMP-based motion imitation, and whole-body trajectory tracking. Future extensions will support general whole-body VLA (Vision-Language-Action) control.

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Features

• Velocity Tracking Control: Omnidirectional speed command tracking for robust locomotion

• AMP (Adversarial Motion Priors): High-quality natural motion imitation learning

• Whole-Body Trajectory Tracking: Accurate task-space and joint-space trajectory following

• Under Development: General whole-body VLA (Vision-Language-Action) control pipeline

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Environment Requirements

• OS: Ubuntu 22.04 / 24.04

• IsaacLab: >=3.0

• Python: 3.12

• CUDA: 12.8 or higher

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Installation

1. Set Up Conda Environment and Dependencies

conda create -n onehbc python=3.12
conda activate onehbc

2. install IsaacLab Newton Physics Engine by following the official installation guide

3. Clone OneHBC

cd IsaacLab
git clone https://github.com/HongtuZ/OneHBC.git
cd OneHBC

4. Install Rsl-rl Library

cd rsl-rl
pip install -e .

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Training Commands

Velocity Tracking Control

# Train
python scripts/rsl_rl/train.py --task OneHBC-RL-Flat-THS23Dof-v0 --viz none --num_envs 4096

# Train with visualization
python scripts/rsl_rl/train.py --task OneHBC-RL-Flat-THS23Dof-v0 --viz newton --num_envs 4096

### AMP Imitation Learning
TODO: Add AMP training and evaluation commands

### Whole\-Body Trajectory Tracking
TODO: Add whole-body trajectory tracking training and evaluation commands