Deep Reinforcement Learning of Robotic Manipulation for Whip Targeting

Xiang Bai, Junyi Wang, Xiaofeng Xiong, Evangelos Boukas

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

This paper aims at a deep reinforcement learning (DRL) controller for fast (< 1.5s) manipulation of a flexible tool (i.e., whip) to hit a target in 3D space. The controller consists of a DRL algorithm for optimizing joint motions, and a proportional-derivative (PD) mechanism for tracking the optimized motions. Their objective is to minimize the distance between the whip-end-tip and the target. The proposed controller was validated in a 7-DOF robot arm by comparing four DRL algorithms in the physical simulator MuJoCo. It shows that the proximal policy optimization (PPO) outperforms others by obtaining the maximum average reward. Notably, PPO can still effectively interact with the environment under sparse or even unrewarding conditions, making it a robust choice for complex and dynamic tasks. Our work provides preliminary knowledge of DRL applications to fast robotic arm control in flexible object manipulation.
Original languageEnglish
Title of host publicationProceedings of 2025 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots
Number of pages6
PublisherIEEE
Publication date2025
Article number10979109
ISBN (Print)979-8-3315-1686-4
DOIs
Publication statusPublished - 2025
Event2025 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots - Palermo, Italy
Duration: 14 Apr 202518 Apr 2025

Conference

Conference2025 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots
Country/TerritoryItaly
CityPalermo
Period14/04/202518/04/2025

Keywords

  • Three-dimensional displays
  • Target tracking
  • Heuristic algorithms
  • Programming
  • Aerospace electronics
  • Deep reinforcement learning
  • Manipulators
  • Optimization
  • Autonomous robots

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