NPMO-Rearrangement

This repository contains the implementation of the Non-prehensile Multi-object (NPMO) Rearrangement system for running on a UR3 Robot from the paper received by the journal Biomimetic Intelligence and Robotics:

Hierarchical Policy with Deep-reinforcement Learning for Nonprehensile Multiobject Rearrangement

Fan Bai, Fei Meng, Jianbang Liu, Jiankun Wang, Max Q.-H. Meng

paper | [Video]()

If you use this work, please cite the following as appropriate:


@article{BAI2022100047,
         title = {Hierarchical policy with deep-reinforcement learning for nonprehensile multiobject rearrangement},
         journal = {Biomimetic Intelligence and Robotics},
         volume = {2},
         number = {3},
         pages = {100047},
         year = {2022},
         issn = {2667-3797},
         doi = {https://doi.org/10.1016/j.birob.2022.100047},
         url = {https://www.sciencedirect.com/science/article/pii/S2667379722000134},
         author = {Fan Bai and Fei Meng and Jianbang Liu and Jiankun Wang and Max Q.-H. Meng},
}

@misc{bai2021hierarchical,
      title={Hierarchical Policy for Non-prehensile Multi-object Rearrangement with Deep Reinforcement Learning and Monte Carlo Tree Search}, 
      author={Fan Bai and Fei Meng and Jianbang Liu and Jiankun Wang and Max Q. -H. Meng},
      year={2021},
      eprint={2109.08973},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
}

Contact

Any questions or comments contact Fan Bai.

Introduction

Non-prehensile multi-object rearrangement is a robotic task of planning feasible paths and transferring multiple objects to their predefined target poses without grasping. It needs to consider how each object reaches the target and the order of object movement, which significantly deepens the complexity of the problem. To address these challenges, We implemented an efficient Hierarchical Policy for Non-prehensile Multi-object (NPMO) Rearrangement, with specific contributions as follows:

We model the NPMO rearrangement task and solve it with a hierarchical policy;
We propose a high-level MCTS policy accelerated by the policy network trained with imitation and reinforcement;
We design a low-level policy to control the robot to achieve path primitives execution.

Method

We propose a hierarchical policy to divide and conquer for non-prehensile multi-object rearrangement. In the high-level policy, guided by a designed policy network, the Monte Carlo Tree Search efficiently searches for the optimal rearrangement sequence among multiple objects, which benefits from imitation and reinforcement. In the low-level policy, the robot plans the paths according to the order of path primitives and manipulates the objects to approach the goal poses one by one.

Experimental results

We verify through experiments that the proposed method can achieve a higher success rate, fewer steps, and shorter path length compared with the state-of-the-art.

Qualitative result of algorithm execution in real robot experiments.

. The sequence of actions is a-j.

Comparing the performance of different methods.

. SR means Success Rate.

The length of action sequence of increased objects for MCTS.

. In each box, black data points are layed over a 1.96 Standard Error of Mean (95% confidence interval) in dark color and a 1 Standard Deviation in light color. Red lines represent mean values.

The impact of different imitation levels on RL.

Training Environment

Install Requirements python 3.6 pytorch 1.7.1 CUDA 10.1 tensorflow 1.15.0
Build Files
```
cd src/utils
g++ -shared -O2 search.cpp --std=c++11 -ldl -fPIC -o search.so
```
The configuration method of these environments can refer to the link

Real robot Environment

Hardware:

This code is designed around a UR3 robot using an Intel Realsense D435 camera mounted on the wrist. A 3D-printalbe camera mount is available in the cad folder. The following external packages are required to run everything completely:

Installation:

Clone this repository into your ROS worksapce and run rosdep install --from-paths src --ignore-src --rosdistro=<your_rosdistro> -y and then catkin_make/catkin build.

Local python requirements can be installed by:

pip install -r requirements.txt

Packages Overview

Running

To run NPMO rearrangement experiments:

# Start the robot and required extras.
roslaunch ur_robot_driver ur3_bringup.launch robot_ip:=192.168.56.101

# Run MoveIt to plan and execute actions on the arm.
roslaunch ur3_moveit_config ur3_moveit_planning_executing.launch

# Start the camera, depth conversion.
roslaunch realsense2_camera rs_aligned_depth.launch

# Publish the pose transformation between the camera and the arm.
roslaunch easy_handeye publish.launch

# Rearrange multiple objects.
rosrun NPMO-Rearrangement run_detection_push.py

Configuration

While this code has been written with specific hardware in mind, different physical settings or cameras may be used by changing some codes. New robots and cameras will require major changes.

baifanxxx / NPMO-Rearrangement

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