Learning Personalized Safety Interventions for Haptic Human-Robot Shared Control

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[Submitted on 21 Jul 2026]

Title:Learning Personalized Safety Interventions for Haptic Human-Robot Shared Control

View a PDF of the paper titled Learning Personalized Safety Interventions for Haptic Human-Robot Shared Control, by Dawei Zhang and 1 other authors

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Abstract:Haptic feedback provides an implicit channel for communicating safety intentions during human-robot shared control. Existing haptic guidance systems typically employ predefined intervention strategies that cannot accommodate the diverse safety preferences of individual users or application scenarios. To address this limitation, we propose a Learning from Haptics (LfH) framework that learns user-preferred safety interventions from sparse demonstrations, eliminating the need for manual trial-and-error design. Our framework is built on a differentiable Control Barrier Function (CBF)-based optimization layer that automatically adjusts the underlying safety parameters to match the demonstrated haptic responses. Instead of tuning controller parameters directly, users teach the system how they expect it to intervene during teleoperation. The resulting haptic guidance reflects the demonstrated intervention preferences while preserving the intuitive interaction of haptic shared control. Simulation and hardware experiments demonstrate that the proposed framework can learn personalized safety interventions from sparse user input and reduce the mismatch between the generated haptic feedback and the demonstrated preferences.

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2607.19534 [cs.RO]

(or arXiv:2607.19534v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.19534

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Dawei Zhang [view email] [v1] Tue, 21 Jul 2026 19:28:41 UTC (3,571 KB)

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