Learn from Interaction: Learning to Pick via Reinforcement Learning in Challenging Clutter

1. Overview

In this work, we present an end-to-end reinforcement learning (RL) framework, which aims at singulating and simultaneously picking the objects one by one from a random clutter. We present a novel solution to incorporate object interaction in policy learning and a gripper designed for this technique is capable of changing relative digit lengths. This repository provides the implementation.

2. Prerequisites

2.1 Hardware requirements

• Universal Robot UR10
• Robotiq 140mm Adaptive parallel-jaw gripper
• RealSense Camera L515
• Extendable Finger for realizing the adjustable finger length. The CAD model can be found here.

2.2 Software requirements

The code is built with Python 3.6. Dependencies are listed in [requirements.yaml] and can be installed via Anaconda by running:

conda env create -n learn_interaction -f requirements.yaml

3. Training

If you want to train your own model, please run the following code:

python main.py --play-only==False

4. Testing

4.1 Test in Simulation

We provide a testing script to evaluate our trained model in simulation. The following code runs the test on three trained objects, and report the average grasp success rates.

python main.py --play-only==True

4.2 Test on Real Robot (UR10)

Here we provide the steps to test our method on a real robot.

Robot control

Robot is controlled via this python software.

Camera setup

To deploy RealSense L515 camera,

1. Download and install the librealsense SDK 2.0
2. Our camera setting can be found in real/640X480_L_short_default.json

Start testing

Then run the following code to start testing:

cd real
python test_in_real.py

Maintenance

For any technical issues, please contact: Chao Zhao (czhaobb@connect.ust.hk).