yubinwang11 / hab-mobile-manipulation

Mobile manipulation in Habitat

Mobile Manipulation for the Home Assistant Benchmark (HAB)

This is a PyTorch implementation of:

Multi-skill Mobile Manipulation for Object Rearrangement
Jiayuan Gu, Devendra Singh Chaplot, Hao Su, Jitendra Malik
UC San Diego, Meta AI Research, UC Berkeley

Project website: https://sites.google.com/view/hab-m3

set_table_teaser.mp4

Table of Contents

• Installation
• Data
• Interactive play
• Evaluation
  • Evaluate a sub-task
  • Evaluate a HAB (Home Assistant Benchmark) task
• Training
• Acknowledgments

Installation

# Ensure the latest submodules
git submodule update --init --recursive
# Create a conda env
conda create -n hab-mm python=3.7
# Activate the conda env
conda activate hab-mm
# Install habitat-sim from source
conda install cmake=3.14.0 patchelf ninja
cd habitat-sim && pip install -r requirements.txt && python setup.py install --bullet --headless && cd ..
# Install habitat-lab
cd habitat-lab && pip install -r requirements.txt && python setup.py develop && cd ..
# Install requirements
pip install -r requirements.txt
# Install habitat manipulation
python setup.py develop
# Post-installation
echo "export MAGNUM_LOG=quiet HABITAT_SIM_LOG=quiet" >> ~/.bashrc

We also provide a docker image: docker pull jiayuangu/hab-mm.

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Known Issues

If you encounter any memory leak during training, please try to install habitat-sim from conda.

# Install pytorch first
conda install -y pytorch==1.5.1 cudatoolkit=10.2 -c pytorch
# Install habitat-sim from aihabitat-nightly
conda install habitat-sim withbullet headless -c conda-forge -c aihabitat-nightly
# Install habitat-sim from source again
cd habitat-sim && pip install -r requirements.txt && python setup.py install --bullet --headless && cd ..

Troubleshooting

• Could not find an EGL device for CUDA device 0: reinstall Nvidia driver

Data

# Download ReplicaCAD v1.4, YCB objects, and Fetch URDF.
python -m habitat_sim.utils.datasets_download --uids rearrange_task_assets
# Generate physical config to correctly configure the simulator backend
python -c "from habitat.datasets.utils import check_and_gen_physics_config; check_and_gen_physics_config()"
# Download generated episodes
pip install gdown
gdown https://drive.google.com/drive/folders/1oEhsiqoWcEA2FNuQd9QfCPKNKgSwHbaW -O data/datasets/rearrange/v3 --folder

To re-generate our episodes, please refer to episode generation.

Interactive play

Interactively play the task with the default config:

python habitat_extensions/tasks/rearrange/play.py

Use i/j/k/l to move the robot end-effector, and w/a/s/d to move the robot base. Use f/g to grasp or release an object.

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Use a specific task config:

python habitat_extensions/tasks/rearrange/play.py  --cfg configs/rearrange/tasks/pick_v1.yaml

Or use a specific RL config:

python habitat_extensions/tasks/rearrange/play.py  --cfg configs/rearrange/skills/tidy_house/pick_v1_joint_SCR.yaml

Evaluation

Evaluate a sub-task

# Evaluate the latest checkpoint of a skill saved at "data/results/rearrange/skills/tidy_house/pick_v1_joint_SCR"
python mobile_manipulation/run_ppo.py --cfg configs/rearrange/skills/tidy_house/pick_v1_joint_SCR.yaml --run-type eval
# Evaluate the latest checkpoint of a skill saved at "data/results/rearrange/skills/tidy_house/pick_v1_joint_SCR/seed=100"
python mobile_manipulation/run_ppo.py --cfg configs/rearrange/skills/tidy_house/pick_v1_joint_SCR.yaml --run-type eval --run-type eval PREFIX seed=100

Pretrained skills can be downloaded here.

pip install gdown
gdown https://drive.google.com/drive/folders/1u7DAd25PE818wjg-MxDKJ7y5n8GQtfrz -O data/results/rearrange/skills --folder

Evaluate a HAB (Home Assistant Benchmark) task

# Interactively visualize results
python mobile_manipulation/eval_composite.py --cfg configs/rearrange/composite/tidy_house/mr.yaml --viewer --render-info
# Save results
# "--no-rgb" can be passed to the script to accelerate if rgb is not used.
python mobile_manipulation/eval_composite.py --cfg configs/rearrange/composite/tidy_house/mr.yaml --save-log
# Save videos
python mobile_manipulation/eval_composite.py --cfg configs/rearrange/composite/tidy_house/mr.yaml --save-video all

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We provide a script to evaluate a HAB task for multiple runs (different training and evaluation seeds).

BG=1 bash scripts/eval_composite.sh configs/rearrange/composite/tidy_house/mr.yaml

To collect evaluation results of multiple runs:

# By default, there will also be a timestamp in the path
python scripts/collect_results.py -d data/results/rearrange/composite/tidy_house/mr

Training

Train an individual skill (e.g., Pick for TidyHouse):

# The result will be saved at "data/results/rearrange/skills/tidy_house/pick_v1_joint_SCR".
python mobile_manipulation/run_ppo.py --cfg configs/rearrange/skills/tidy_house/pick_v1_joint_SCR.yaml --run-type train
# Specify a prefix and a random seed for the experiment.
# The result will be saved at "data/results/rearrange/skills/tidy_house/pick_v1_joint_SCR/seed=101" 
python mobile_manipulation/run_ppo.py --cfg configs/rearrange/skills/tidy_house/pick_v1_joint_SCR.yaml --run-type train PREFIX seed=101 TASK_CONFIG.SEED 101

Acknowledgments

This repository is inspired by Habitat Lab for RL environments and PPO implementation. We would also like to thank Andrew Szot and Alexander Clegg for their help in using Habitat 2.0.