Robotic Grasping and Placement Controlled by EEG-Based Hybrid Visual and Motor Imagery
This work addresses the challenge of intuitive human-robot interaction for BCI-driven robotics, though it appears incremental as it builds on existing EEG and robotic control methods.
The paper tackles the problem of enabling real-time, intention-driven robotic grasping and placement using EEG-based visual and motor imagery, achieving online decoding accuracies of 40.23% (VI) and 62.59% (MI) with an end-to-end task success rate of 20.88%.
We present a framework that integrates EEG-based visual and motor imagery (VI/MI) with robotic control to enable real-time, intention-driven grasping and placement. Motivated by the promise of BCI-driven robotics to enhance human-robot interaction, this system bridges neural signals with physical control by deploying offline-pretrained decoders in a zero-shot manner within an online streaming pipeline. This establishes a dual-channel intent interface that translates visual intent into robotic actions, with VI identifying objects for grasping and MI determining placement poses, enabling intuitive control over both what to grasp and where to place. The system operates solely on EEG via a cue-free imagery protocol, achieving integration and online validation. Implemented on a Base robotic platform and evaluated across diverse scenarios, including occluded targets or varying participant postures, the system achieves online decoding accuracies of 40.23% (VI) and 62.59% (MI), with an end-to-end task success rate of 20.88%. These results demonstrate that high-level visual cognition can be decoded in real time and translated into executable robot commands, bridging the gap between neural signals and physical interaction, and validating the flexibility of a purely imagery-based BCI paradigm for practical human-robot collaboration.