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arXiv · 2611.07235

Adaptive Hybrid Force-Vision Control with EMG-Derived References for Deformable Bottle Manipulation

Jeffrey Lee·Geoffrey Hanks·Evangeline Bouganim·Yue Hu
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Abstract

Robotic automation of laboratory tasks has become a growing interest to remove researchers from hazardous environments and repetitive procedures. However, even equipment as simple as squeeze bottles poses a challenging manipulation problem due to its deformability. This paper presents an adaptive hybrid vision-force controller for robotic grippers to effectively manipulate laboratory squeeze bottles to dispense liquid. This approach integrates force and vision feedback according to the interaction state, enabling effective squeezing while accounting for bottle deformation and recovery. The proposed controller is validated by comparing its performance with a pure force controller on 12 combinations of conditions consisting of 2 target dispense volumes, 2 plastic squeeze bottles, and 1 to 3 squeezes used to dispense. Human demonstrations of these operating conditions were collected using electromyography (EMG) sensing mapped to a desired grip force, which was then used as the force reference for the controllers to squeeze the bottles. Results demonstrated that the proposed adaptive hybrid vision-force controller achieved more accurate liquid dispensing with a mean absolute error (MAE) of 6.54 mL compared to a MAE of 15.92 mL from the pure force controller, while also replicating the human action recorded from the EMG signal more closely.

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Jeffrey Lee, Geoffrey Hanks, Evangeline Bouganim, Yue Hu. 2026-11-04. Adaptive Hybrid Force-Vision Control with EMG-Derived References for Deformable Bottle Manipulation. https://arxiv.org/abs/2611.07235

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