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Investigating Motor Skill Learning Processes with a Robotic Manipulandum

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Cooperative Object Transportation Using Curriculum-Based Deep Reinforcement Learning.

Gyuho Eoh¹, Tae-Hyoung Park¹

¹Industrial AI Research Center, Chungbuk National University, Cheongju 28116, Korea.

Sensors (Basel, Switzerland)

|July 24, 2021

Summary

This study introduces curriculum-based deep reinforcement learning (DRL) for cooperative object transportation. The proposed region-growing and single- to multi-robot curricula significantly improve learning efficiency and success rates for robotic manipulation tasks.

Keywords:

cooperative object transportation curriculum deep reinforcement learning policy-reuse region-growing

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Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Traditional object transportation methods require complex control strategies.
Deep reinforcement learning (DRL) offers a promising alternative for robotic manipulation.
DRL-based transportation can be slow to learn and prone to failure due to random exploration.

Purpose of the Study:

To develop efficient learning techniques for cooperative object transportation using DRL.
To address the challenges of slow learning and policy failure in DRL-based robotic tasks.
To introduce novel curricula for enhanced DRL policy learning.

Main Methods:

Implementation of a region-growing curriculum to progressively expand the object's initial region.
Development of a single- to multi-robot curriculum to leverage pre-trained policies.
Utilizing deep reinforcement learning for cooperative object transportation.

Main Results:

The region-growing curriculum demonstrated increased success probability by restricting the initial working area.
The single- to multi-robot curriculum facilitated policy learning through transfer learning.
Simulation results validated the effectiveness of the proposed curriculum-based DRL approach.

Conclusions:

The proposed curricula significantly enhance the efficiency and success rate of DRL-based cooperative object transportation.
Curriculum learning provides a structured approach to overcome learning challenges in complex robotic tasks.
This work contributes to advancing autonomous robotic manipulation capabilities.