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

Related tags

Deep Learning reinforcement-learning robotics motion-planning robot-learning robot-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)

You might also like...
Public implementation of "Learning from Suboptimal Demonstration via Self-Supervised Reward Regression" from CoRL'21

Self-Supervised Reward Regression (SSRR) Codebase for CoRL 2021 paper "Learning from Suboptimal Demonstration via Self-Supervised Reward Regression "

null 19 Dec 12, 2022
Local trajectory planner based on a multilayer graph framework for autonomous race vehicles.
Local trajectory planner based on a multilayer graph framework for autonomous race vehicles.

Graph-Based Local Trajectory Planner The graph-based local trajectory planner is python-based and comes with open interfaces as well as debug, visuali

TUM - Institute of Automotive Technology 160 Jan 4, 2023
Convert Mission Planner (ArduCopter) Waypoint Missions to Litchi CSV Format to execute on DJI Drones
Convert Mission Planner (ArduCopter) Waypoint Missions to Litchi CSV Format to execute on DJI Drones

Mission Planner to Litchi Convert Mission Planner (ArduCopter) Waypoint Surveys to Litchi CSV Format to execute on DJI Drones Litchi doesn't support S

Yaros 24 Dec 9, 2022
This is a vision-based 3d model manipulation and control UI
This is a vision-based 3d model manipulation and control UI

Manipulation of 3D Models Using Hand Gesture This program allows user to manipulation 3D models (.obj format) with their hands. The project support bo

Cortic Technology Corp. 43 Oct 23, 2022
XtremeDistil framework for distilling/compressing massive multilingual neural network models to tiny and efficient models for AI at scale

XtremeDistilTransformers for Distilling Massive Multilingual Neural Networks ACL 2020 Microsoft Research [Paper] [Video] Releasing [XtremeDistilTransf

Microsoft 125 Jan 4, 2023
PyTorch source code for Distilling Knowledge by Mimicking Features

LSHFM.detection This is the PyTorch source code for Distilling Knowledge by Mimicking Features. And this project contains code for object detection wi

Guo-Hua Wang 4 Dec 17, 2022
Adversarial-Information-Bottleneck - Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck (NeurIPS21)
Adversarial-Information-Bottleneck - Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck (NeurIPS21)

NeurIPS 2021 Title: Distilling Robust and Non-Robust Features in Adversarial Exa

LBK 35 Dec 26, 2022
[ICML 2020] Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control
[ICML 2020] Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

PG-MORL This repository contains the implementation for the paper Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Contro

MIT Graphics Group 65 Jan 7, 2023
Control-Robot-Arm-using-PS4-Controller - A Robotic Arm based on Raspberry Pi and Arduino that controlled by PS4 Controller

Control-Robot-Arm-using-PS4-Controller You can see all details about this Robot

MohammadReza Sharifi 5 Jan 1, 2022
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 https://clvrai.com/mopa-pd
Control-Raspberry-Pi-Robot-using-Hand-Gestures - A 4WD Robot car based on Raspberry Pi that controlled by hand gestures(using openCV and mediapipe)

Control-Raspberry-Pi-Robot-using-Hand-Gestures you can see all details about thi

MohammadReza Sharifi 27 Dec 13, 2022
PaddleRobotics is an open-source algorithm library for robots based on Paddle, including open-source parts such as human-robot interaction, complex motion control, environment perception, SLAM positioning, and navigation.

简体中文 | English PaddleRobotics paddleRobotics是基于paddle的机器人开源算法库集，包括人机交互、复杂运动控制、环境感知、slam定位导航等开源算法部分。 人机交互 主动多模交互技术TFVT-HRI 主动多模交互技术是通过视觉、语音、触摸传感器等输入机器人

null 185 Dec 26, 2022
O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning (CoRL 2021)

O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning Object-object Interaction Affordance Learning. For a given object-object int

Kaichun Mo 26 Nov 4, 2022
ManiSkill-Learn is a framework for training agents on SAPIEN Open-Source Manipulation Skill Challenge (ManiSkill Challenge), a large-scale learning-from-demonstrations benchmark for object manipulation.

ManiSkill-Learn ManiSkill-Learn is a framework for training agents on SAPIEN Open-Source Manipulation Skill Challenge, a large-scale learning-from-dem

Hao Su's Lab, UCSD 48 Dec 30, 2022
Attention-driven Robot Manipulation (ARM) which includes Q-attention

Attention-driven Robotic Manipulation (ARM) This codebase is home to: Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulation I

Stephen James 84 Dec 29, 2022
Guiding evolutionary strategies by (inaccurate) differentiable robot simulators @ NeurIPS, 4th Robot Learning Workshop

Guiding Evolutionary Strategies by Differentiable Robot Simulators In recent years, Evolutionary Strategies were actively explored in robotic tasks fo

Vladislav Kurenkov 4 Dec 14, 2021
Space robot - (Course Project) Using the space robot to capture the target satellite that is disabled and spinning, then stabilize and fix it up

Space robot - (Course Project) Using the space robot to capture the target satellite that is disabled and spinning, then stabilize and fix it up

Mingrui Yu 3 Jan 7, 2022
Code for ICCV 2021 paper "Distilling Holistic Knowledge with Graph Neural Networks"

HKD Code for ICCV 2021 paper "Distilling Holistic Knowledge with Graph Neural Networks" cifia-100 result The implementation of compared methods are ba

Wang Yucheng 30 Dec 18, 2022
Deep Structured Instance Graph for Distilling Object Detectors (ICCV 2021)

DSIG Deep Structured Instance Graph for Distilling Object Detectors Authors: Yixin Chen, Pengguang Chen, Shu Liu, Liwei Wang, Jiaya Jia. [pdf] [slide]

DV Lab 31 Nov 17, 2022
Official implementation of "Accelerating Reinforcement Learning with Learned Skill Priors", Pertsch et al., CoRL 2020

Accelerating Reinforcement Learning with Learned Skill Priors [Project Website] [Paper] Karl Pertsch1, Youngwoon Lee1, Joseph Lim1 1CLVR Lab, Universi

Cognitive Learning for Vision and Robotics (CLVR) lab @ USC 134 Dec 6, 2022