Guiding Soft Robots with Motor-Imagery Brain Signals and Impedance Control
Maximilian St\"olzle, Sonal Santosh Baberwal, Daniela Rus, Shirley Coyle, and Cosimo Della Santina

TL;DR
This paper presents a novel brain-controlled soft robot system using minimal EEG channels and impedance control, enabling safe, real-time manipulation of soft robots for complex tasks.
Contribution
It introduces a new pipeline combining EEG-based motor imagery with impedance control tailored for soft robots, addressing nonlinearities and safety in human-robot interaction.
Findings
66% success rate in reaching setpoints
Average response time of 21.5 seconds for successful steps
Demonstrated real-world task execution with soft robots
Abstract
Integrating Brain-Machine Interfaces into non-clinical applications like robot motion control remains difficult - despite remarkable advancements in clinical settings. Specifically, EEG-based motor imagery systems are still error-prone, posing safety risks when rigid robots operate near humans. This work presents an alternative pathway towards safe and effective operation by combining wearable EEG with physically embodied safety in soft robots. We introduce and test a pipeline that allows a user to move a soft robot's end effector in real time via brain waves that are measured by as few as three EEG channels. A robust motor imagery algorithm interprets the user's intentions to move the position of a virtual attractor to which the end effector is attracted, thanks to a new Cartesian impedance controller. We specifically focus here on planar soft robot-based architected metamaterials,…
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Taxonomy
TopicsMicro and Nano Robotics · Soft Robotics and Applications · EEG and Brain-Computer Interfaces
