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Learning to Centralize Dual-Arm Assembly
[article]
2021
arXiv
pre-print
Robotic manipulators are widely used in modern manufacturing processes. However, their deployment in unstructured environments remains an open problem. To deal with the variety, complexity, and uncertainty of real-world manipulation tasks, it is essential to develop a flexible framework with reduced assumptions on the environment characteristics. In recent years, reinforcement learning (RL) has shown great results for single-arm robotic manipulation. However, research focusing on dual-arm
arXiv:2110.04003v2
fatcat:hivy3ctji5e27cyyxe3ivhzj3a