Lightweight Learning from Actuation-Space Demonstrations via Flow Matching for Whole-Body Soft Robotic Grasping
Yang, Liudi Bai, Yang Wang, Yuhao Alsarraj, Ibrahim Kutyniok, Gitta Wang, Zhanchi Wu, Ke
Yang, Liudi
Bai, Yang
Wang, Yuhao
Alsarraj, Ibrahim
Kutyniok, Gitta
Wang, Zhanchi
Wu, Ke
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Robotics
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Journal article
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English
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Abstract
Robotic grasping under uncertainty remains a fundamental challenge due to its uncertain and contact-rich nature. Traditional rigid robotic hands, with limited degrees of freedom and compliance, rely on complex model-based and heavy feedback controllers to manage such interactions. Soft robots, by contrast, exhibit embodied mechanical intelligence: their underactuated structures and passive flexibility of their whole body naturally accommodate uncertain contacts and enable adaptive behaviors. To harness this capability, we propose a lightweight actuation-space learning framework that infers distributional control representations for whole-body soft robotic grasping directly from deterministic demonstrations using a flow matching model (Rectified Flow), without requiring dense sensing or heavy control loops. Trained with only 30 demonstrations covering less than 8% of the reachable workspace, the learned policy achieved a 97.5% grasp success rate over 1000 trials in simulation. In real-world experiments on 50 uniformly distributed targets, the policy achieved a 100% success rate, generalized to object size variations from -33% to +100%, and remained stable under execution-time scaling from 20% to 200%. These results demonstrate that actuation-space learning effectively embeds mechanical intelligence into control, significantly reducing reliance on centralized computation for grasping under uncertainty.
Citation
L. Yang, Y. Bai, Y. Wang, I. Alsarraj, G. Kutyniok, Z. Wang , et al., "Lightweight Learning from Actuation-Space Demonstrations via Flow Matching for Whole-Body Soft Robotic Grasping," IEEE Robotics and Automation Letters, vol. PP, no. 99, pp. 1-8, 2026, https://doi.org/10.1109/lra.2026.3677747.
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IEEE Robotics and Automation Letters
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Keywords
40 Engineering, 4007 Control Engineering, Mechatronics and Robotics, 46 Information and Computing Sciences, 4602 Artificial Intelligence
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IEEE