ArticleNature machine intelligence2025
Bioinspired trajectory modulation for effective slip control in robot manipulation.
Article in Nature machine intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.