amazon-far / php_parkour

# [RSS 2026] Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching
Zhen Wu\*, Xiaoyu Huang\*, Lujie Yang\*, Yuanhang Zhang, Xi Chen,
Pieter Abbeel†, Rocky Duan†, Angjoo Kanazawa†, Carmelo Sferrazza†, Guanya Shi†, C. Karen Liu†
Project page ·
arXiv ·
Video
## Overview
PHP (Perceptive Humanoid Parkour) enables humanoid robots to autonomously
perform long-horizon, vision-based parkour across challenging obstacle courses.
The framework has three steps:
1. **Motion matching.** Compose retargeted atomic human skills into long-horizon
 trajectories offline using motion matching.
2. **Teacher training.** Train RL experts to track the trajectories with privileged
 state and terrain observations.
3. **Student training.** Distill the experts into a depth-based, multi-skill policy
 using DAgger + RL.
## Get started
For sim2sim with an existing ONNX pair, use the PHP launchers and pinned
Holosoma setup.
For motion matching and training, clone PHP with its pinned Holosoma submodule:
 [code] 
Choose a guide below for environment setup and commands. For training, use the
prepared example datasets or generate your own
with motion matching. If you already have an exported ONNX pair, go directly to sim2sim.
| Guide | Workflow | Requirements |
|---|---|---|
| Motion matching | Generate and inspect motion/terrain datasets; upload them for training or add new skills | Conda; no IsaacSim |
| Training and evaluation | Train teachers, distill a student, evaluate checkpoints, and export ONNX | Linux/NVIDIA with IsaacSim |
| Sim2sim | Run the exported depth policy in MuJoCo | Holosoma's MuJoCo and inference environments |
## 🏃 Try the demo
To try the example student policy in MuJoCo, download the validated ONNX pair
from the student release.
Run from the PHP checkout root:
 [code] 
The release contains sanitized `depth_backbone.onnx` and `student.onnx` files,
without raw `.pt` or teacher checkpoints. Files default to
`~/.cache/php-parkour/student`; pass `--destination DIR` to choose another
directory. The downloader verifies the archive and both model files against
release-assets.json.
Follow the sim2sim guide to run the policy. You can
also use the automatically exported ONNX pair
from your own student training run.
## Community projects
- **PHP on mjlab** —
 A community port by @yhx1203.
 See the announcement and videos.
## Citation
 [code] 
Related: OmniRetarget.