InsActor:
Instruction-driven Physics-based Characters

Jiawei Ren^* Mingyuan Zhang^* Cunjun Yu^* Xiao Ma Liang Pan Ziwei Liu^†

S-Lab, Nanyang Technological University National University of Singapore
Dyson Robot Learning Lab
^*equal contribution
^†corresponding author

NeurIPS 2023

[Project Page] • [Paper] • [Video]

Installation

conda create -n insactor python==3.9 -y
conda activate insactor

# diffmimic
python -m pip install --upgrade "jax[cuda]==0.4.2" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

# diffplanner
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch -y
conda install -c bottler nvidiacub -y
conda install -c fvcore -c iopath -c conda-forge fvcore iopath -y
conda install pytorch3d -c pytorch3d -y

python -m pip install -r requirements.txt

export PYTHONPATH=$PYTHONPATH:$(pwd)

download models

mkdir pretrained_models && cd pretrained_models
gdown --fuzzy https://drive.google.com/file/d/1qdglrZJa5ago2nkSWc5Nu52kvLaS_dTg/view?usp=drive_link # Human-ML skills 0.001
gdown --fuzzy https://drive.google.com/file/d/1Wi33YZWR8K6IuWZdcXdV2mqcivcyObIU/view?usp=drive_link # Human-ML planner
cd ..

Run Demo

streamlit run tools/demo.py --server.port 8501

The WebUI should be available at localhost:8501.

Run evaluation

#prepare data
mkdir data/datasets
gdown https://drive.google.com/file/d/1knGb1Kt9VUu377vcXIDfQN_qcAYSYPez/view?usp=drive_link --fuzzy && unzip human_pml3d.zip && rm human_pml3d.zip &&  mv human_pml3d data/datasets/human_pml3d
gdown https://drive.google.com/file/d/1_YQwV5kqZgSHhOJ_V9MwDjKO5yYfsdDP/view?usp=drive_link --fuzzy && unzip kit_pml.zip && rm kit_pml.zip &&  mv kit_pml data/datasets/kit_pml
# prepare contrasitive models
mkdir data/evaluators
gdown https://drive.google.com/file/d/1aoiq702L6fy4yCsX_ub1MxD_mvj-VmrQ/view?usp=drive_link --fuzzy && mv humanml.pth data/evaluators
gdown https://drive.google.com/file/d/1fyGAi2NHHvUNgDN0YgB1ND_7buVel_SA/view?usp=drive_link --fuzzy &&  mv kit.pth data/evaluators
# KIT
export CONTROLLER_PARAM_PATH="pretrained_models/skill_kit_0.01.pkl"
python -u tools/test.py configs/planner/kit.py --work-dir=work_dirs/eval --physmode=normal pretrained_models/planner_kit.pth
# Humanml
export CONTROLLER_PARAM_PATH="pretrained_models/skill_human_0.01.pkl"
python -u tools/test.py configs/planner/human.py --work-dir=work_dirs/eval --physmode=normal pretrained_models/planner_humanml.pth
# Humanml (Perturb)
export CONTROLLER_PARAM_PATH="pretrained_models/skill_human_0.01.pkl"
python -u tools/test.py configs/planner/human.py --work-dir=work_dirs/eval --physmode=normal --perturb true pretrained_models/planner_humanml.pth
# Humanml (Waypoint)
export CONTROLLER_PARAM_PATH="pretrained_models/skill_human_0.01.pkl"
python -u tools/test_waypoint.py configs/planner/human.py --work-dir=work_dirs/eval --physmode=normal pretrained_models/planner_humanml.pth

Difference from the paper results:

We improved the low-level trakcer training by adding a gradient truncation.
We fixed the contrastive model for humanml-3d.

More models

cd pretrained_models
gdown --fuzzy https://drive.google.com/file/d/10gFEWUdZtMIA-6yhd_gZXbYm9snMZ63m/view?usp=drive_link # KIT-ML skills 0.001
gdown --fuzzy https://drive.google.com/file/d/1oyT5DE5ItZb1KNSV0lW85cLk9w4alGPV/view?usp=drive_link # Human-ML skills 0.0
gdown --fuzzy https://drive.google.com/file/d/1fvW3RtFU8sOGj2rLZTT7gGSOzLCvNkUW/view?usp=drive_link # KIT-ML skills 0.0
gdown --fuzzy https://drive.google.com/file/d/17ut3gymJpDrPt4nsIhPcug0A0HevqGkE/view?usp=drive_link # Human-ML skills 0.01
gdown --fuzzy https://drive.google.com/file/d/1Ijosu_4W2eIg72kK2IuGaR0wbEhxw6CU/view?usp=drive_link # KIT-ML skills 0.01
gdown --fuzzy https://drive.google.com/file/d/10WrKJ4v1u6DwCYb8KT9s2aP3n2BBn9ST/view?usp=drive_link # KIT-ML planner
cd ..

Visualize Evaluation Results

The evaluation script will save diffusion plans at planned_traj.npy and simulated motion in simulated_traj.npy.

streamlit run visualize.py

Then input the trajectory file path (e.g., planned_traj.npy), or drag and drop the trajectory file.

Training Low-level Controllers

#prepare data
gdown https://drive.google.com/file/d/15qjv-tREix2kJ5kvaRgxTXgoKQgHPq6L/view?usp=drive_link --fuzzy && unzip kit_ml_raw_processed.zip && rm kit_ml_raw_processed.zip &&  mv kit_ml_raw_processed data/kit_ml_raw_processed  # KIT
gdown https://drive.google.com/file/d/1WCiuqQOeIpu2sFjnF20aJiNmYLFMkNxU/view?usp=drive_link --fuzzy && unzip humanml3d_processed.zip && rm humanml3d_processed.zip &&  mv humanml3d_processed data/humanml3d_processed  # Human-ML
# train
python mimic.py --config configs/controller/human_0.01.yaml

Training Diffusion Policy

python -u tools/train.py configs/planner/kit.py --work-dir=planner_kit  # kit
python -u tools/train.py configs/planner/human.py --work-dir=planner_human  # humanml

Citation

@article{ren2023insactor,
  title={InsActor: Instruction-driven Physics-based Characters},
  author={Ren, Jiawei and Zhang, Mingyuan and Yu, Cunjun and Ma, Xiao and Pan, Liang and Liu, Ziwei},
  journal={NeurIPS},
  year={2023}
}

jiawei-ren/insactor

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