Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation (CoRL 2021)

Related tags

Deep Learningreinforcement-learningroboticsmotion-planningrobot-learningrobot-manipulation
Overview

Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation

[Project website] [Paper]

This project is a PyTorch implementation of Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation, published in CoRL 2021.

Learning complex manipulation tasks in realistic, obstructed environments is a challenging problem due to hard exploration in the presence of obstacles and high-dimensional visual observations. Prior work tackles the exploration problem by integrating motion planning and reinforcement learning. However, the motion planner augmented policy requires access to state information, which is often not available in the real-world settings. To this end, we propose to distill the state-based motion planner augmented policy to a visual control policy via (1) visual behavioral cloning to remove the motion planner dependency along with its jittery motion, and (2) vision-based reinforcement learning with the guidance of the smoothed trajectories from the behavioral cloning agent. We validate our proposed approach on three manipulation tasks in obstructed environments and show its high sample-efficiency, outperforming state-of-the-art algorithms for visual policy learning.

Prerequisites

Installation

  1. Install Mujoco 2.0 and add the following environment variables into ~/.bashrc or ~/.zshrc.
# Download mujoco 2.0
$ wget https://www.roboti.us/download/mujoco200_linux.zip -O mujoco.zip
$ unzip mujoco.zip -d ~/.mujoco
$ mv ~/.mujoco/mujoco200_linux ~/.mujoco/mujoco200

# Copy mujoco license key `mjkey.txt` to `~/.mujoco`

# Add mujoco to LD_LIBRARY_PATH
$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/.mujoco/mujoco200/bin

# For GPU rendering (replace 418 with your nvidia driver version)
$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/nvidia-418

# Only for a headless server
$ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libGLEW.so:/usr/lib/nvidia-418/libGL.so
  1. Download this repository and install python dependencies
# Install system packages
sudo apt-get install libgl1-mesa-dev libgl1-mesa-glx libosmesa6-dev patchelf libopenmpi-dev libglew-dev python3-pip python3-numpy python3-scipy

# Create out/ folder for saving/loading RL checkpoints
cd mopa-pd
mkdir out

# Create checkpoints/ folder for saving/loading BC-Visual checkpoints
mkdir checkpoints

# Install required python packages in your new env
pip install -r requirements.txt
  1. Install ompl
# Linux
sudo apt install libyaml-cpp-dev
sh ./scripts/misc/installEigen.sh #from the home directory # install Eigen
sudo apt-get install libboost-all-dev # install Boost C++ for ompl

# Mac OS
brew install libyaml yaml-cpp
brew install eigen

# Build ompl
git clone [email protected]:ompl/ompl.git ../ompl
cd ../ompl
cmake .
sudo make install

# if ompl-x.x (x.x is the version) is installed in /usr/local/include, you need to rename it to ompl
mv /usr/local/include/ompl-x.x /usr/local/include/ompl
  1. Build motion planner python wrapper
cd ./mopa-pd/motion_planners
python setup.py build_ext --inplace
  1. Configure wandb for tracking experiments (optional)
  • Sign up for a free account at https://app.wandb.ai/login?signup=true.
  • Open this file: config/__init__.py
  • Set wandb argument to True.
  • Add your username to the entity argument.
  • Add your project name to the project argument.
  1. Servers without a monitor (optional)

You may use the following code to create a virtual monitor for rendering.

# Run the next line for Ubuntu
$ sudo apt-get install xserver-xorg libglu1-mesa-dev freeglut3-dev mesa-common-dev libxmu-dev libxi-dev

# Configure nvidia-x
$ sudo nvidia-xconfig -a --use-display-device=None --virtual=1280x1024

# Launch a virtual display
$ sudo /usr/bin/X :1 &

# Run a command with DISPLAY=:1
DISPLAY=:1 <command>

Available environments

SawyerPushObstacle-v0 SawyerLiftObstacle-v0 SawyerAssemblyObstacle-v0
Sawyer Push Sawyer Lift Sawyer Assembly

How to run experiments

Launch a virtual display (only for a headless server)

sudo /usr/bin/X :1 &

MoPA-RL

# train MoPA-RL policy
sh ./scripts/3d/assembly/mopa.sh 0 1234
sh ./scripts/3d/lift/mopa.sh 0 1234
sh ./scripts/3d/push/mopa.sh 0 1234

# evaluate MoPA-RL policy
sh ./scripts/3d/assembly/mopa_eval.sh 0 1234
sh ./scripts/3d/lift/mopa_eval.sh 0 1234
sh ./scripts/3d/push/mopa_eval.sh 0 1234

# generate MoPA-RL data for BC-Visual using trained MoPA-RL's checkpoint
sh ./scripts/3d/assembly/run_multiple_sh.sh
sh ./scripts/3d/lift/run_multiple_sh.sh
sh ./scripts/3d/push/run_multiple_sh.sh

BC-Visual

# pre-process MoPA-RL data
python util/state_img_preprocessing.py

cd rl # must be inside rl folder to execute the following commands

# bc_visual_args.py is the config for training and evaluating BC-Visual

# train BC-Visual 
python behavioral_cloning_visual.py

# evaluate BC-Visual
python evaluate_bc_visual.py

Baselines and Ours

  • Sawyer Push
###### Training
sh ./scripts/3d/push/bcrl_stochastic_two_buffers.sh 0 1234 # Ours
sh ./scripts/3d/push/bcrl_stochastic_two_buffers_mopa.sh 0 1234 # Ours (w/o BC Smoothing)
sh ./scripts/3d/push/bcrl_stochastic_two_buffers_dr.sh 0 1234 # Ours (w DR)
sh ./scripts/3d/push/bcrl_mopa_sota.sh 0 1234 # CoL 
sh ./scripts/3d/push/bcrl_sota.sh 0 1234 # CoL (w BC Smoothing) 
sh ./scripts/3d/push/mopa_asym.sh 0 1234 # MoPA Asym. SAC
sh ./scripts/3d/push/bcrl_stochastic_randweights.sh 0 1234 # Asym. SAC

###### Evaluation
sh ./scripts/3d/push/bcrl_stochastic_two_buffers_eval.sh 0 1234 # Ours
sh ./scripts/3d/push/bcrl_stochastic_two_buffers_mopa_eval.sh 0 1234 # Ours (w/o BC Smoothing)
sh ./scripts/3d/push/bcrl_stochastic_two_buffers_dr_eval.sh 0 1234 # Ours (w DR)
sh ./scripts/3d/push/bcrl_mopa_sota_eval.sh 0 1234 # CoL
sh ./scripts/3d/push/bcrl_sota_eval.sh 0 1234 # CoL (w BC Smoothing)
sh ./scripts/3d/push/mopa_asym_eval.sh 0 1234  # MoPA Asym. SAC
sh ./scripts/3d/push/bcrl_stochastic_randweights_eval.sh 0 1234 # Asym. SAC
  • Sawyer Lift
###### Training
sh ./scripts/3d/lift/bcrl_stochastic_two_buffers.sh 0 1234 # Ours
sh ./scripts/3d/lift/bcrl_stochastic_two_buffers_mopa.sh 0 1234 # Ours (w/o BC Smoothing)
sh ./scripts/3d/lift/bcrl_stochastic_two_buffers_dr.sh 0 1234 # Ours (w DR)
sh ./scripts/3d/lift/bcrl_mopa_sota.sh 0 1234 # CoL
sh ./scripts/3d/lift/bcrl_sota.sh 0 1234 # CoL (w BC Smoothing) 
sh ./scripts/3d/lift/mopa_asym.sh 0 1234 # MoPA Asym. SAC
sh ./scripts/3d/lift/bcrl_stochastic_randweights.sh 0 1234 # Asym. SAC

###### Evaluation
sh ./scripts/3d/lift/bcrl_stochastic_two_buffers_eval.sh 0 1234 # Ours
sh ./scripts/3d/lift/bcrl_stochastic_two_buffers_mopa_eval.sh 0 1234 # Ours (w/o BC Smoothing)
sh ./scripts/3d/lift/bcrl_stochastic_two_buffers_dr_eval.sh 0 1234 # Ours (w DR)
sh ./scripts/3d/lift/bcrl_mopa_sota_eval.sh 0 1234 # CoL
sh ./scripts/3d/lift/bcrl_sota_eval.sh 0 1234 # CoL (w BC Smoothing)
sh ./scripts/3d/lift/mopa_asym_eval.sh 0 1234 # MoPA Asym. SAC
sh ./scripts/3d/lift/bcrl_stochastic_randweights_eval.sh 0 1234 # Asym. SAC
  • Sawyer Assembly
###### Training
sh ./scripts/3d/assembly/bcrl_stochastic_two_buffers.sh 0 1234 # Ours
sh ./scripts/3d/assembly/bcrl_stochastic_two_buffers_mopa.sh 0 1234 # Ours (w/o BC Smoothing)
sh ./scripts/3d/assembly/bcrl_stochastic_two_buffers_dr.sh 0 1234 # Ours (w DR)
sh ./scripts/3d/assembly/bcrl_mopa_sota.sh 0 1234 # CoL
sh ./scripts/3d/assembly/bcrl_sota.sh 0 1234 # CoL (w BC Smoothing)
sh ./scripts/3d/assembly/mopa_asym.sh 0 1234 # MoPA Asym. SAC
sh ./scripts/3d/assembly/bcrl_stochastic_randweights.sh 0 1234 # Asym. SAC

###### Evaluation
sh ./scripts/3d/assembly/bcrl_stochastic_two_buffers_eval.sh 0 1234 # Ours
sh ./scripts/3d/assembly/bcrl_stochastic_two_buffers_mopa_eval.sh 0 1234 # Ours (w/o BC Smoothing)
sh ./scripts/3d/assembly/bcrl_stochastic_two_buffers_dr_eval.sh 0 1234 # Ours (w DR)
sh ./scripts/3d/assembly/bcrl_mopa_sota_eval.sh 0 1234 # CoL
sh ./scripts/3d/assembly/bcrl_sota_eval.sh 0 1234 # CoL (w BC Smoothing) 
sh ./scripts/3d/assembly/mopa_asym_eval.sh 0 1234 # MoPA Asym. SAC
sh ./scripts/3d/assembly/bcrl_stochastic_randweights_eval.sh 0 1234 # Asym. SAC

Domain Randomization

To run experiments with domain randomized simulation, the following parameters can be set in config:

  • dr: set to True to train the model with domain randomization
  • dr_params_set: choose as per the training environment - ["sawyer_push, sawyer_lift, sawyer_assembly]
  • dr_eval: set to True for evaluating the domain randomization model

Directories

The structure of the repository:

  • rl: Reinforcement learning code
  • env: Environment code for simulated experiments (2D Push and all Sawyer tasks)
  • config: Configuration files
  • util: Utility code
  • motion_planners: Motion planner code from MoPA-RL
  • scripts: Scripts for all experiments

Log directories:

  • logs/rl.ENV.DATE.PREFIX.SEED:
    • cmd.sh: A command used for running a job
    • git.txt: Log gitdiff
    • prarms.json: Summary of parameters
    • video: Generated evaulation videos (every evalute_interval)
    • wandb: Training summary of W&B, like tensorboard summary
    • ckpt_*.pt: Stored checkpoints (every ckpt_interval)
    • replay_*.pt: Stored replay buffers (every ckpt_interval)

Trouble shooting

Mujoco GPU rendering

To use GPU rendering for mujoco, you need to add /usr/lib/nvidia-000 (000 should be replaced with your NVIDIA driver version) to LD_LIBRARY_PATH before installing mujoco-py. Then, during mujoco-py compilation, it will show you linuxgpuextension instead of linuxcpuextension. In Ubuntu 18.04, you may encounter an GL-related error while building mujoco-py, open venv/lib/python3.7/site-packages/mujoco_py/gl/eglshim.c and comment line 5 #include <GL/gl.h> and line 7 #include <GL/glext.h>.

Virtual display on headless machines

On servers, you don’t have a monitor. Use this to get a virtual monitor for rendering and put DISPLAY=:1 in front of a command.

# Run the next line for Ubuntu
$ sudo apt-get install xserver-xorg libglu1-mesa-dev freeglut3-dev mesa-common-dev libxmu-dev libxi-dev

# Configure nvidia-x
$ sudo nvidia-xconfig -a --use-display-device=None --virtual=1280x1024

# Launch a virtual display
$ sudo /usr/bin/X :1 &

# Run a command with DISPLAY=:1
DISPLAY=:1 <command>

pybind11-dev not found

wget http://archive.ubuntu.com/ubuntu/pool/universe/p/pybind11/pybind11-dev_2.2.4-2_all.deb
sudo apt install ./pybind11-dev_2.2.4-2_all.deb

ERROR: GLEW initalization error: Missing GL version

This issue is most likely due to running on a headless server.

Solution 1:

sudo mkdir -p /usr/lib/nvidia-000

Then add this line to ~/.bashrc file:
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/nvidia-000

Solution 2:

1. First import and call mujocopy_render_hack in main.py
2. Follow the instructions in "Virtual display on headless machines" section
3. When running a script, remember to add DISPLAY:=1 <command>

/usr/bin/ld: cannot find -lGL

Source: https://stackoverflow.com/questions/33447653/usr-bin-ld-cannot-find-lgl-ubuntu-14-04

sudo rm /usr/lib/x86_64-linux-gnu/libGL.so 
sudo ln -s /usr/lib/libGL.so.1 /usr/lib/x86_64-linux-gnu/libGL.so

References

Citation

If you find this useful, please cite

@inproceedings{liu2021mopa,
  title={Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation},
  author={I-Chun Arthur Liu and Shagun Uppal and Gaurav S. Sukhatme and Joseph J. Lim and Peter Englert and Youngwoon Lee},
  booktitle={Conference on Robot Learning},
  year={2021}
}

Authors

I-Chun (Arthur) Liu*, Shagun Uppal*, Gaurav S. Sukhatme, Joseph J. Lim, Peter Englert, and Youngwoon Lee at USC CLVR and USC RESL (*Equal contribution)

Owner
Cognitive Learning for Vision and Robotics (CLVR) lab @ USC
Learning and Reasoning for Artificial Intelligence, especially focused on perception and action. Led by Professor Joseph J. Lim @ USC
Cognitive Learning for Vision and Robotics (CLVR) lab @ USC
GitHub Repository https://clvrai.com/mopa-pd
Build Low Code Automated Tensorflow, What-IF explainable models in just 3 lines of code.

Build Low Code Automated Tensorflow explainable models in just 3 lines of code.

Hasan Rafiq 170 Dec 26, 2022
This is an official implementation for "Self-Supervised Learning with Swin Transformers".

Self-Supervised Learning with Vision Transformers By Zhenda Xie*, Yutong Lin*, Zhuliang Yao, Zheng Zhang, Qi Dai, Yue Cao and Han Hu This repo is the

Swin Transformer 529 Jan 02, 2023
Code & Data for the Paper "Time Masking for Temporal Language Models", WSDM 2022

Time Masking for Temporal Language Models This repository provides a reference implementation of the paper: Time Masking for Temporal Language Models

Guy Rosin 12 Jan 06, 2023
This is the source code for: Context-aware Entity Typing in Knowledge Graphs.

This is the source code for: Context-aware Entity Typing in Knowledge Graphs.

9 Sep 01, 2022
some classic model used to segment the medical images like CT、X-ray and so on

github_project This is a project for medical image segmentation. This project includes common medical image segmentation models such as U-net, FCN, De

2 Mar 30, 2022
Fuzzing tool (TFuzz): a fuzzing tool based on program transformation

T-Fuzz T-Fuzz consists of 2 components: Fuzzing tool (TFuzz): a fuzzing tool based on program transformation Crash Analyzer (CrashAnalyzer): a tool th

HexHive 244 Nov 09, 2022
Official PyTorch implementation of the paper "Deep Constrained Least Squares for Blind Image Super-Resolution", CVPR 2022.

Deep Constrained Least Squares for Blind Image Super-Resolution [Paper] This is the official implementation of 'Deep Constrained Least Squares for Bli

MEGVII Research 141 Dec 30, 2022
Alleviating Over-segmentation Errors by Detecting Action Boundaries

Alleviating Over-segmentation Errors by Detecting Action Boundaries Forked from ASRF offical code. This repo is the a implementation of replacing orig

13 Dec 12, 2022
PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet

PyTorch Image Classification Following papers are implemented using PyTorch. ResNet (1512.03385) ResNet-preact (1603.05027) WRN (1605.07146) DenseNet

1.2k Jan 04, 2023
Official code for Next Check-ins Prediction via History and Friendship on Location-Based Social Networks (MDM 2018)

MUC Next Check-ins Prediction via History and Friendship on Location-Based Social Networks (MDM 2018) Performance Details for Accuracy: | Dataset

Yijun Su 3 Oct 09, 2022
A collection of resources and papers on Diffusion Models, a darkhorse in the field of Generative Models

This repository contains a collection of resources and papers on Diffusion Models and Score-based Models. If there are any missing valuable resources

5.1k Jan 08, 2023
Code for Ditto: Building Digital Twins of Articulated Objects from Interaction

Ditto: Building Digital Twins of Articulated Objects from Interaction Zhenyu Jiang, Cheng-Chun Hsu, Yuke Zhu CVPR 2022, Oral Project | arxiv News 2022

UT Robot Perception and Learning Lab 78 Dec 22, 2022
Centroid-UNet is deep neural network model to detect centroids from satellite images.

Centroid UNet - Locating Object Centroids in Aerial/Serial Images Introduction Centroid-UNet is deep neural network model to detect centroids from Aer

GIC-AIT 19 Dec 08, 2022
Chinese named entity recognization with BiLSTM using Keras

Chinese named entity recognization (Bilstm with Keras) Project Structure ./ ├── README.md ├── data │   ├── README.md │   ├── data 数据集 │   │   ├─

1 Dec 17, 2021
FridaHookAppTool - Frida Hook App Tool With Python

FridaHookAppTool（以下是Hook mpaas框架的例子） mpaas移动开发框架ios端抓包hook脚本 使用方法：链接数据线，开启burp设置

13 Nov 30, 2022
PanopticBEV - Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images

Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images This r

63 Dec 16, 2022
PiRank: Learning to Rank via Differentiable Sorting

PiRank: Learning to Rank via Differentiable Sorting This repository provides a reference implementation for learning PiRank-based models as described

54 Dec 17, 2022
AdvStyle - Official PyTorch Implementation

AdvStyle - Official PyTorch Implementation Paper | Supp Discovering Interpretable Latent Space Directions of GANs Beyond Binary Attributes. Huiting Ya

Beryl 37 Oct 21, 2022
Backdoor Attack through Frequency Domain

Backdoor Attack through Frequency Domain DEPENDENCIES python==3.8.3 numpy==1.19.4 tensorflow==2.4.0 opencv==4.5.1 idx2numpy==1.2.3 pytorch==1.7.0 Data

5 Jun 18, 2022
NeoPlay is the project dedicated to ESport events.

NeoPlay is the project dedicated to ESport events. On this platform users can participate in tournaments with prize pools as well as create their own tournaments.

3 Dec 18, 2021