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 language | English |
---|---|
Title of host publication | Proceedings of 2025 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots |
Number of pages | 6 |
Publisher | IEEE |
Publication date | 2025 |
Article number | 10979109 |
ISBN (Print) | 979-8-3315-1686-4 |
DOIs | |
Publication status | Published - 2025 |
Event | 2025 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots - Palermo, Italy Duration: 14 Apr 2025 → 18 Apr 2025 |
Conference
Conference | 2025 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots |
---|---|
Country/Territory | Italy |
City | Palermo |
Period | 14/04/2025 → 18/04/2025 |
Keywords
- Three-dimensional displays
- Target tracking
- Heuristic algorithms
- Programming
- Aerospace electronics
- Deep reinforcement learning
- Manipulators
- Optimization
- Autonomous robots