Awesome

Hierarchical Diffusion Policy

This repo contains the PyTorch implementation of the CVPR 2024 paper

Hierarchical Diffusion Policy for Multi-Task Robotic Manipulation

Xiao Ma, Sumit Patidar, Iain Haughton, Stephen James

CVPR 2024

Dyson Robot Learning Lab

HDP factorises a manipulation policy into a hierarchical structure: a high-level task-planning agent which predicts a distant next-best end-effector pose (NBP), and a low-level goal-conditioned diffusion policy which generates optimal motion trajectories. The factorised policy representation allows HDP to tackle both long-horizon task planning while generating fine-grained low-level actions. To generate context-aware motion trajectories while satisfying robot kinematics constraints, we present a novel kinematics-aware goal-conditioned control agent, Robot Kinematics Diffuser (RK-Diffuser). Specifically, RK-Diffuser learns to generate both the end-effector pose and joint position trajectories, and distill the accurate but kinematics-unaware end-effector pose diffuser to the kinematics-aware but less accurate joint position diffuser via differentiable kinematics.

In this repository, we provide the code for training the low-level RK-Diffuser. We use PerAct as our high-level agent and we refer to its official implementation for detailed training configurations. We also include the evaluation code for the HDP architecture.

For more details, see our project page.

Installation

conda create -n hdp python=3.10
conda activate hdp
bash ./extra_scripts/install_coppeliasim.sh
conda install pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia
pip install cffi==1.15
pip install -r requirements.txt
python setup.py develop

Running in headless mode

Please refer to the official guide of RLBench.

Generate the dataset

First, we need to generate the training dataset.

python extra_scripts/dataset_generator.py --save_path=<your dataset path> --tasks=<your task> --variations=1 --processes=1 --episodes_per_task=100

For example, to generate a dataset for the reach_target task,

python extra_scripts/dataset_generator.py --save_path=/data/${USER}/rlbench --tasks=reach_target --variations=1 --processes=1 --episodes_per_task=100

The script will generate both train and eval datasets at the same time.

Training the low-level RK-Diffuser

To train the low-level sim agent, simply do

python3 train_low_level.py env=sim env.data_path=<your dataset path> env.tasks="[<task1>, <task2>, ...]"

For example, to train a model for the reach_target and the take_lid_off_saucepan tasks, run

python3 train_low_level.py env=sim env.data_path=/data/${USER}/rlbench env.tasks="[reach_target, take_lid_off_saucepan]"

You can enable online logging or set wandb run name by adding the following args

log=True run_name=<your run name>

Evaluate the models

We also provide the full evaluation pipeline to HDP. To run the evaluation, simply do

python eval.py rlbench.tasks="[<your task>]" rlbench.headless=False method.model_path=<path to rk-diffuser ckpt> framework.logdir=<path to peract ckpt dir>

Citation

@article{ma2024hierarchical,
  author    = {Ma, Xiao and Patidar, Sumit and Haughton, Iain and James, Stephen},
  title     = {Hierarchical Diffusion Policy for Kinematics-Aware Multi-Task Robotic Manipulation},
  journal   = {CVPR},
  year      = {2024},
}

Credits

This repository is adapted from PerAct and decision diffusers.