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Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation.

Dong Han¹, Beni Mulyana¹, Vladimir Stankovic²

¹School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.

Sensors (Basel, Switzerland)

|April 13, 2023

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Deep reinforcement learning (DRL) significantly advances robotic manipulation, including grasping and object handling. This review covers DRL algorithms, challenges, solutions, and future research directions for robotic tasks.

Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Robotic manipulation tasks like grasping and object handling present significant challenges.
Deep reinforcement learning (DRL) has emerged as a powerful tool for addressing these challenges.

Purpose of the Study:

To provide a comprehensive overview of recent advances in DRL algorithms for robotic manipulation.
To discuss the fundamental concepts of reinforcement learning and its system components.

Main Methods:

Reviewing various DRL algorithms, including value-based, policy-based, and actor-critic methods.
Examining challenges encountered when applying DRL to robotics.
Analyzing proposed solutions to these challenges.

Keywords:

graph neural network reinforcement learning robotic manipulation

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Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Main Results:

DRL systems have shown success in robotic manipulation tasks.
Key DRL algorithms and their applications in robotics are detailed.
Common issues and their resolutions in DRL for robotics are identified.

Conclusions:

DRL is a key enabler for sophisticated robotic manipulation.
Further research is needed to address remaining challenges and explore future directions in DRL for robotics.