为什么值得关注

它将多种先进方法（SONIC、ADD、BPO 等）统一到一个可配置的框架中，提供预训练策略和地形运动优化器，并支持 Isaac Lab 和 MuJoCo 两种后端。

适合谁使用

机器人强化学习研究人员
类人机器人运动开发者
Isaac Lab 模拟用户
对运动模仿和地形适应感兴趣的研究者

典型使用场景

为 Unitree G1 机器人训练运动跟踪策略
在平坦和不平坦地形上评估模仿学习算法
使用 MPPI 优化器生成适应地形的运动片段
从教师网络蒸馏基于视觉的策略

项目优势

配置驱动的任务组合，支持独立轴选择
提供全面的预训练检查点和演示运动数据
集成了多种先进算法（对抗模仿、跨具身等）
包含地形运动优化器（TCRS）用于自适应片段生成

使用前须知

需要预先安装 Isaac Lab（非独立运行）
仅直接支持 Unitree G1 机器人
地形、视觉和蒸馏任务仅支持 Isaac Lab 后端；MuJoCo 后端任务支持有限且需微调才能转移检查点

README 快速开始

PMT — Perceptive Motion Tracking

Project page: https://acodedog.github.io/perceptive-bfm/

Cartwheel
Backflip

PMT trains humanoid motion-tracking RL policies for the Unitree G1 on Isaac Lab: DeepMimic-style imitation, vision/terrain perception, cross-embodiment (SONIC), adversarial imitation (ADD), and teacher → distill → finetune pipelines. Everything is config-driven: a task selects independent axes (robot / terrain / motion / obs / reward / network / algorithm / stage) and the builder derives the coupled ones, failing loud on invalid combos.

What's in this repo

Training & playback — scripts/train.py / scripts/play.py over a catalog of ~25 G1 tasks (Task catalog), config-composed from configs/ by the pmt_tasks/ task layer.
Pretrained policies — ready-to-run G1 checkpoints under checkpoints/pretrained/.
Demo motion data — a 99-pair sample of raw/optimized terrain clips + terrain meshes under assets/ so the terrain/distill tasks run out-of-the-box.
TCRS — an MPPI terrain-motion optimizer (TCRS/) that generates the optimized clips from raw motion + a terrain scene.
MuJoCo backend — the flat task family also runs on mjlab (MuJoCo-Warp); see docs/MJLAB_USAGE.md.

Scope: this is a research/training codebase. It runs inside an existing Isaac Lab install (it does not vendor Isaac Sim / Isaac Lab) — see Install.

Install

PMT runs inside an Isaac Lab Python environment — it does not vendor Isaac Sim / Isaac Lab. Get a working Isaac Lab install first, then install PMT (editable) into that env:

conda activate            # the env that has Isaac Lab + Isaac Sim
export OMNI_KIT_ACCEPT_EULA=YES          # accept the Omniverse EULA for headless launches
cd /path/to/PMT
python -m pip install -e .               # installs the motion_tracking_rl RL core

Run all commands from the repository root (pmt_tasks/, configs/, scripts/ are used in place).

Prerequisites: an importable isaaclab / isaaclab_tasks, Python ≥ 3.10, the Unitree G1 robot assets (resolved via PMT_ASSET_DIR — not vendored), and per-task motion *.npz clips.

**Pat

项目描述

Official implementation of Perceptive Behavior Foundation Model

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