Robust Model-Based In-Hand Manipulation with Integrated Real-Time Motion-Contact Planning and Tracking
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915277746208768 |
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| author | Jiang, Yongpeng Yu, Mingrui Zhu, Xinghao Tomizuka, Masayoshi Li, Xiang |
| author_facet | Jiang, Yongpeng Yu, Mingrui Zhu, Xinghao Tomizuka, Masayoshi Li, Xiang |
| contents | Robotic dexterous in-hand manipulation, where multiple fingers dynamically make and break contact, represents a step toward human-like dexterity in real-world robotic applications. Unlike learning-based approaches that rely on large-scale training or extensive data collection for each specific task, model-based methods offer an efficient alternative. Their online computing nature allows for ready application to new tasks without extensive retraining. However, due to the complexity of physical contacts, existing model-based methods encounter challenges in efficient online planning and handling modeling errors, which limit their practical applications. To advance the effectiveness and robustness of model-based contact-rich in-hand manipulation, this paper proposes a novel integrated framework that mitigates these limitations. The integration involves two key aspects: 1) integrated real-time planning and tracking achieved by a hierarchical structure; and 2) joint optimization of motions and contacts achieved by integrated motion-contact modeling. Specifically, at the high level, finger motion and contact force references are jointly generated using contact-implicit model predictive control. The high-level module facilitates real-time planning and disturbance recovery. At the low level, these integrated references are concurrently tracked using a hand force-motion model and actual tactile feedback. The low-level module compensates for modeling errors and enhances the robustness of manipulation. Extensive experiments demonstrate that our approach outperforms existing model-based methods in terms of accuracy, robustness, and real-time performance. Our method successfully completes five challenging tasks in real-world environments, even under appreciable external disturbances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04978 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Model-Based In-Hand Manipulation with Integrated Real-Time Motion-Contact Planning and Tracking Jiang, Yongpeng Yu, Mingrui Zhu, Xinghao Tomizuka, Masayoshi Li, Xiang Robotics Robotic dexterous in-hand manipulation, where multiple fingers dynamically make and break contact, represents a step toward human-like dexterity in real-world robotic applications. Unlike learning-based approaches that rely on large-scale training or extensive data collection for each specific task, model-based methods offer an efficient alternative. Their online computing nature allows for ready application to new tasks without extensive retraining. However, due to the complexity of physical contacts, existing model-based methods encounter challenges in efficient online planning and handling modeling errors, which limit their practical applications. To advance the effectiveness and robustness of model-based contact-rich in-hand manipulation, this paper proposes a novel integrated framework that mitigates these limitations. The integration involves two key aspects: 1) integrated real-time planning and tracking achieved by a hierarchical structure; and 2) joint optimization of motions and contacts achieved by integrated motion-contact modeling. Specifically, at the high level, finger motion and contact force references are jointly generated using contact-implicit model predictive control. The high-level module facilitates real-time planning and disturbance recovery. At the low level, these integrated references are concurrently tracked using a hand force-motion model and actual tactile feedback. The low-level module compensates for modeling errors and enhances the robustness of manipulation. Extensive experiments demonstrate that our approach outperforms existing model-based methods in terms of accuracy, robustness, and real-time performance. Our method successfully completes five challenging tasks in real-world environments, even under appreciable external disturbances. |
| title | Robust Model-Based In-Hand Manipulation with Integrated Real-Time Motion-Contact Planning and Tracking |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.04978 |