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This research focuses on optimizing the haptic interaction parameters of a robotic guide dog by adapting filter stiffness based on terrain slope, directly addressing core challenges in embodied AI and robot learning for assistive applications. The study demonstrates that dynamic adjustment of impedance control significantly improves user preference metrics during physical guidance tasks, marking a tangible advancement in human-robot collaboration.
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What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Understanding User Preferences of a Slope-Aware Variable-Admittance Filter for a Robot Guide Dog

arXiv2 min read

arXiv:2609.15362v1 Announce Type: new Abstract: This letter investigates how the parameters of a slope-aware variable-admittance filter influence user preferences in force-based interaction with a robotic guide dog for visually impaired individuals. The proposed system consists of a quadruped robot equipped with a sensor-free rigid handle for physical guidance. The framework combines path following, momentum-based interaction-wrench estimation, and a variable-admittance filter whose stiffness and damping are adapted online from slope information extracted by the robot's depth camera. The adaptation policies are evaluated through high-fidelity simulations and a human-subject study involving blindfolded sighted participants. Multiple strategies are compared using a Taguchi L9 design of experiments. Preliminary main-effect results suggest that increasing stiffness uphill and decreasing it downhill improves both objective and subjective metrics, whereas damping shows no significant main effect.

Abstract:This letter investigates how the parameters of a slope-aware variable-admittance filter influence user preferences in force-based interaction with a robotic guide dog for visually impaired individuals. The proposed system consists of a quadruped robot equipped with a sensor-free rigid handle for physical guidance. The framework combines path following, momentum-based interaction-wrench estimation, and a variable-admittance filter whose stiffness and damping are adapted online from slope information extracted by the robot's depth camera. The adaptation policies are evaluated through high-fidelity simulations and a human-subject study involving blindfolded sighted participants. Multiple strategies are compared using a Taguchi L9 design of experiments. Preliminary main-effect results suggest that increasing stiffness uphill and decreasing it downhill improves both objective and subjective metrics, whereas damping shows no significant main effect.

Robotics (cs.RO)

arXiv:2609.15362 [cs.RO]

(or arXiv:2609.15362v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.15362 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 216 words

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