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Conference Paper
A Multi-Agent-Based Real-World Robotic Manipulation System
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- Authors
- Samyeul Noh, Hyonyoung Han
- Issue Date
- 2022-07
- Citation
- International Conference on Ubiquitous Robots (UR) 2022, pp.470-473
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- This paper proposes a system framework that can be applied to multi-agent-based real-world robotic manipulation systems in the field of manufacturing. The proposed system framework enables real-world robotic manipulators to perform a cooperative task not only through calculated trajectories by an inverse kinematics solver in real-world environments but also through learned behaviors by deep reinforcement learning algorithms in simulated environments. Here, the cooperative task refers to a task that requires two or more robotic manipulators to perform the task. To this end, the system consists of four main blocks: multi-agent-based real-world robotic manipulation ROS framework, real-world robotic environment, simulated robotic environment, and sim-to-real interfacing. The system has been tested in real-world environments under two cooperative tasks “peg-in-hole” and “pick-and-stack” to validate its feasibility on multi-agent-based real-world robotic manipulation.
- KSP Keywords
- Deep reinforcement learning, Peg-in-hole, Real-world, Reinforcement Learning(RL), Robotic manipulation system, System Framework, agent-based, cooperative task, inverse kinematics, learning algorithms, multi-agent