SamCon

PyBullet implementation of SamCon (SIGGRAPH 2010 "Sampling-based Contact-rich Motion Control").

SamCon is an easy-to-understand method for physics-based humanoid motion tracking.
This repo has these following features:

• Algorithms: (1) samples generation, simulation and evaluation, (2) elite samples selection, (3) optimal trajectory searching.
• Humanoid URDF with skeleton similar to SMPL.
• Scripts of obtaining reference motion from large-scale motion dataset AMASS.
• CPU-based parallelism (master-worker framework, tested on windows & ubuntu).

Seeing demo videos in this page [demo] ▶️

Hope this repo can contribute to the physics-based character animation community. 😇

What's SamCon?

Given a reference motion (shown by the transparent humanoid), directly tracking it by using PD controllers will always lead the simulated humanoid to fall down.

Running SamCon to correct the reference motion, we can get a optimal control trajectory. Tracking it again (PD controllers are still used), the simulated humanoid will behave like the reference motion.

Getting Started

Installation

git clone https://github.com/liangpan99/SamCon.git
pip install -r requirements.txt

code tested on windows & ubuntu

Reference motion

We use a large-scale 3D human motion dataset, i.e. AMASS, as the reference motion database.

To use pre-processed reference motion (only contains 252 sequences of "ACCAD"), download from google drive and place in the directory ./data/motion/.

To manually generate reference motion from AMASS, follow the following instructions:

1. download AMASS dataset, and organize it into the following structure:

AMASS
|   - ACCAD
|         - Female1General_c3d
|                            - A1 - Stand_poses.npz
|                            - A2 - Sway_poses.npz
|                            - A2 - Sway t2_poses.npz
|                            - ...
|         - Female1Gestures_c3d
|         - Female1Running_c3d
|         - ...
|   - BioMotionLab_NTroje
|   - BMLhandball
|   - ...

2. download amass_copycat_occlusion.pkl from google drive and place in the directory ./data/motion/. It's an annotation file that help avoid invalid motion sequences in AMASS, such as sitting on a chair, provided by Kin-Poly.

3. change amass_dir & sequences variables in file process_amass_raw.py, one indicates the path to AMASS dataset and the other indicates sequences chosen to process. Then, run:

python process_amass/process_amass_raw.py
python process_amass/amass_to_bullet.py

4. visualize reference motion:

python process_amass/vis_motion.py

It will ask you to input a sequence name, all names are lied in ./data/motion/all_seq_names.txt, you can pick one. Press [Q] to change sequence.

Examples

We provide two configs (walk & cartwheel) and corresponding results to show you how to use this repo. All hyper-parameters are lied in .yml config, e.g. nIter, nSample, nSave and so on. Note that, because code requires a lot of disk I/O, please set tmp_dir to SSD disk for speeding up.

Highly recommended to "evaluate SamCon" first (all requirements are included in this repo).
"Run SamCon" will take you a little time to prepare reference motion.

Run SamCon

python scripts/run_samcon.py --cfg walk --num_processes 8
python scripts/run_samcon.py --cfg cartwheel --num_processes 8

maximum number of num_processes is equal to your computer's CPU cores

Evaluate SamCon

python scripts/eval_samcon.py --cfg walk --file "walk_ACCAD_Female1Walking_c3d_B12 - walk turn right (90)_poses.pkl"
python scripts/eval_samcon.py --cfg walk --file "walk_ACCAD_Male1Walking_c3d_Walk B10 - Walk turn left 45_poses.pkl"
python scripts/eval_samcon.py --cfg cartwheel --file "cartwheel_ACCAD_Female1Gestures_c3d_D6- CartWheel_poses.pkl"

You can find cost distribution images in the directory ./results/samcon/cfg_name/info/.

References

Algorithm: SamCon paper + Zhihu tutorial
Humanoid URDF: ScaDiver
Process AMASS: Kin-Poly