Book ; Online: Reinforcement Learning with Parameterized Manipulation Primitives for Robotic Assembly
2023
Abstract: A common theme in robot assembly is the adoption of Manipulation Primitives as the atomic motion to compose assembly strategy, typically in the form of a state machine or a graph. While this approach has shown great performance and robustness in ... ...
| Abstract | A common theme in robot assembly is the adoption of Manipulation Primitives as the atomic motion to compose assembly strategy, typically in the form of a state machine or a graph. While this approach has shown great performance and robustness in increasingly complex assembly tasks, the state machine has to be engineered manually in most cases. Such hard-coded strategies will fail to handle unexpected situations that are not considered in the design. To address this issue, we propose to find dynamics sequence of manipulation primitives through Reinforcement Learning. Leveraging parameterized manipulation primitives, the proposed method greatly improves both assembly performance and sample efficiency of Reinforcement Learning compared to a previous work using non-parameterized manipulation primitives. In practice, our method achieves good zero-shot sim-to-real performance on high-precision peg insertion tasks with different geometry, clearance, and material. Comment: arXiv admin note: text overlap with arXiv:2011.00778 |
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| Keywords | Computer Science - Robotics |
| Subject code | 629 |
| Publishing date | 2023-06-11 |
| Publishing country | us |
| Document type | Book ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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