Adaptive Hybrid Force-Vision Control with EMG-Derived References for Deformable Bottle Manipulation
Overview
This paper presents an adaptive hybrid vision-force controller for robotic manipulation of deformable laboratory squeeze bottles. The system integrates force and vision feedback based on interaction states to handle bottle deformation and recovery. Human demonstrations were collected via electromyography (EMG) sensing, mapped to desired grip forces, and used as references for the controllers. Validation across 12 condition combinations (varying target dispense volumes, plastic bottle types, and squeeze counts) showed that the proposed controller achieved a mean absolute error of 6.54 mL compared to 15.92 mL for a pure force controller, while more closely replicating human actions recorded from EMG signals.
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Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · mean absolute error (MAE): 6.54 other · Basis not reported · Differing source assertions
“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”
Exact source · revision 1Source owner not reported
Reported quantity · mean absolute error (MAE): 15.92 other · Basis not reported · Differing source assertions
“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.”
Exact source · revision 1Source owner not reported
Reported quantity · target dispense volumes: 2 other · Basis not reported
“ate 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”
Exact source · revision 1Source owner not reported
Reported quantity · plastic squeeze bottles: 2 other · Basis not reported
“ate 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”
Exact source · revision 1Source owner not reported
Reported quantity · squeezes used to dispense: 2 other · Basis not reported
“ate 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”
Exact source · revision 1Source owner not reported
Reported quantity · combinations of conditions: 12 other · Basis not reported
“ate 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”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalAdaptive Hybrid Force-Vision Control with EMG-Derived References for Deformable Bottle Manipulation
This paper presents an adaptive hybrid vision-force controller for robotic manipulation of deformable laboratory squeeze bottles. The system integrates force and vision feedback based on interaction states to handle bottle deformation and recovery. Human demonstrations were collected via electromyography (EMG) sensing, mapped to desired grip forces, and used as references for the controllers. Validation across 12 condition combinations (varying target dispense volumes, plastic bottle types, and squeeze counts) showed that the proposed controller achieved a mean absolute error of 6.54 mL compared to 15.92 mL for a pure force controller, while more closely replicating human actions recorded from EMG signals.
Event coverage history
Current attention 3·Peak within the comparable range 10（2026-11-06 06:00 UTC）·Change within the comparable range over 24 hours -49%
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