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Control UR5 robot with reinforcement learning in CoppeliaSim

Python 100.00%

coppeliasim reinforcement-learning ur5-robot pytorch

pushing-and-grasping-with-reinforcement-learning's Introduction

This work is based on the project visual-pushing-grasping control UR5 robot in CoppeliaSim(V-REP)

I do some major changes focus on reducing computation complexity by using lightweight network and a different way of modeling action space, reward.

[10/12/2023] Update test script and weight

Update test script and pretrained weight
Test result video

[25/11/2023] Update two head grasp model

Only for grasping action
Using mobilenetv2 as backbone and 2 prediction head (1 for 16 orientation, 1 for 112x112 location of grasping action)

TODO:

Update evalution script
Using ROS replace V-REP python api
Increase location map to 224x224 to improve precision
Add more 1 prediction head for pushing/grasping

[24/11/2023] Single branch, end-to-end pipeline

End-to-end pipeline, single branch, replace densenet121 with mobilenetv2
No input rotation, modeling action space as a 3D tensor 112x112x16 (height map resolution=4mm, 8 angle rotations)
Only use RGB as input of network, depth information for z position

CoppeliaSim simulation

The simulation scene when training train_twoheadgraspnet.py, robot successfuly learn to find the object and do grasping action. Due to the limit of resolution (4mm instead of 2mm in original work), location prediction is sometime inaccurate. There is no pushing action so robot find difficult to handle complex scenerios. The scene is recorded during training phase so there are random actions in sequence of actions.

Training result

Training result of two head grasp model

Requirements

CoppeliaSim v4.5.1 linux
Pytorch

Setup

Open simulation/simulation.ttt in CoppeliaSim
Run python train_twoheadgraspnet.py

Note

This repository is under experimenting and developing period
Need to do more expreriment with one head model

Reference

[1] https://github.com/andyzeng/visual-pushing-grasping
[2] Mnih, Volodymyr, et al. "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013).

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