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Related Experiment Video

Updated: Jul 25, 2025

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

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Published on: October 14, 2017

The Task Decomposition and Dedicated Reward-System-Based Reinforcement Learning Algorithm for Pick-and-Place.

Byeongjun Kim¹, Gunam Kwon¹, Chaneun Park²

¹Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.

Biomimetics (Basel, Switzerland)

|June 27, 2023

Summary

This study introduces a reinforcement learning algorithm for robot pick-and-place tasks. The method decomposes the task into subtasks, achieving a 93.2% success rate in simulations.

Keywords:

Pick-and-Place Soft Actor-Critic deep reinforcement learning robot manipulator task decomposition

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Last Updated: Jul 25, 2025

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Investigating Motor Skill Learning Processes with a Robotic Manipulandum

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Published on: February 12, 2017

Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Robot manipulators perform high-level tasks like pick-and-place.
Existing methods may struggle with efficient task decomposition and reward systems.

Purpose of the Study:

To propose a reinforcement learning algorithm for robot pick-and-place tasks.
To enhance grasping success through a dedicated reward system.

Main Methods:

Task decomposition into reaching and grasping subtasks.
Soft Actor-Critic (SAC) for training reaching policies.
Axis-based reward system for object approach.

Main Results:

The proposed algorithm achieved an average success rate of 93.2% in simulations.
Successful pick-and-place operations were demonstrated in the MuJoCo physics engine.

Conclusions:

The task decomposition and reward system effectively improve pick-and-place performance.
The method offers a robust approach for robotic manipulation tasks.