CushionCatch: A Compliant Catching Mechanism for Mobile Manipulators via Combined Optimization and Learning
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866918143132172288 |
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| author | Chen, Bingjie Fan, Keyu Yang, Qi Cheng, Yi Liu, Houde Dong, Kangkang Xia, Chongkun Han, Liang Liang, Bin |
| author_facet | Chen, Bingjie Fan, Keyu Yang, Qi Cheng, Yi Liu, Houde Dong, Kangkang Xia, Chongkun Han, Liang Liang, Bin |
| contents | Catching flying objects with a cushioning process is a skill commonly performed by humans, yet it remains a significant challenge for robots. In this paper, we present a framework that combines optimization and learning to achieve compliant catching on mobile manipulators (CCMM). First, we propose a high-level capture planner for mobile manipulators (MM) that calculates the optimal capture point and joint configuration. Next, the pre-catching (PRC) planner ensures the robot reaches the target joint configuration as quickly as possible. To learn compliant catching strategies, we propose a network that leverages the strengths of LSTM for capturing temporal dependencies and positional encoding for spatial context (P-LSTM). This network is designed to effectively learn compliant strategies from human demonstrations. Following this, the post-catching (POC) planner tracks the compliant sequence output by the P-LSTM while avoiding potential collisions due to structural differences between humans and robots. We validate the CCMM framework through both simulated and real-world ball-catching scenarios, achieving a success rate of 98.70% in simulation, 92.59% in real-world tests, and a 28.7% reduction in impact torques. The open source code has be released for the reference of the community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14754 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CushionCatch: A Compliant Catching Mechanism for Mobile Manipulators via Combined Optimization and Learning Chen, Bingjie Fan, Keyu Yang, Qi Cheng, Yi Liu, Houde Dong, Kangkang Xia, Chongkun Han, Liang Liang, Bin Robotics Catching flying objects with a cushioning process is a skill commonly performed by humans, yet it remains a significant challenge for robots. In this paper, we present a framework that combines optimization and learning to achieve compliant catching on mobile manipulators (CCMM). First, we propose a high-level capture planner for mobile manipulators (MM) that calculates the optimal capture point and joint configuration. Next, the pre-catching (PRC) planner ensures the robot reaches the target joint configuration as quickly as possible. To learn compliant catching strategies, we propose a network that leverages the strengths of LSTM for capturing temporal dependencies and positional encoding for spatial context (P-LSTM). This network is designed to effectively learn compliant strategies from human demonstrations. Following this, the post-catching (POC) planner tracks the compliant sequence output by the P-LSTM while avoiding potential collisions due to structural differences between humans and robots. We validate the CCMM framework through both simulated and real-world ball-catching scenarios, achieving a success rate of 98.70% in simulation, 92.59% in real-world tests, and a 28.7% reduction in impact torques. The open source code has be released for the reference of the community. |
| title | CushionCatch: A Compliant Catching Mechanism for Mobile Manipulators via Combined Optimization and Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.14754 |