Article Synopsis
- Ensuring stable robot manipulation is crucial, and while traditional methods rely on grip force control, this study introduces trajectory modulation as a more effective alternative for slip prevention in specific tasks.
- Researchers developed and evaluated a slip control policy using trajectory modulation and found it outperforms conventional grip force methods in certain scenarios.
- The study emphasizes the importance of integrating a data-driven model within a predictive control framework to optimize trajectory modulation, ultimately enhancing grip stability and adaptability for robots in dynamic environments.
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Article Abstract
Ensuring a stable grasp during robotic manipulation is essential for dexterous and reliable performance. Traditionally, slip control has relied on grip force modulation. Here we show that trajectory modulation provides an effective alternative for slip prevention in certain robotic manipulation tasks. We develop and compare a slip control policy based on trajectory modulation with a conventional grip-force-based approach. Our results demonstrate that trajectory modulation can significantly outperform grip force control in specific scenarios, highlighting its potential as a robust slip control strategy. Furthermore, we show that, similar to humans, incorporating a data-driven action-conditioned forward model within a model predictive control framework is key to optimizing trajectory modulation for slip prevention. These findings introduce a predictive control framework leveraging trajectory adaptation, offering a new perspective on slip mitigation. This approach enhances grasp stability in dynamic and unstructured environments, improving the adaptability of robotic systems across various applications.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12283357 | PMC |
| http://dx.doi.org/10.1038/s42256-025-01062-2 | DOI Listing |
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