An offline approach to fNIRS-guided reinforcement learning for robot behavior
Overview
This research paper explores using functional near-infrared spectroscopy (fNIRS) brain signals to modulate robot learning in simulation. The authors compare agents trained on passive versus active interaction tasks and test methods for enhancing the RL algorithm with neural signals, focusing on parameter augmentation. Results show that the framework is effective, particularly when augmenting trajectory priorities and state-action q-targets, and it successfully learns from offline data, providing a practical alternative for settings where real-time BCI setups are impractical.
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Environment: simulation
“arXiv:2607.14393v3 Announce Type: replace Abstract: Human-in-the-loop Reinforcement Learning has become a popular approach for training, finetuning, and aligning robot behavior with user preferences. Our paper explores the feasibility of using brain signals via functional near-infrared spectroscopy (fNIRS) to modulate robot learning in simulation. We compare agents trained on passive (observational) versus active (de”
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- arXiv Robotics — research abstractsSignalAn offline approach to fNIRS-guided reinforcement learning for robot behavior
This research paper explores using functional near-infrared spectroscopy (fNIRS) brain signals to modulate robot learning in simulation. The authors compare agents trained on passive versus active interaction tasks and test methods for enhancing the RL algorithm with neural signals, focusing on parameter augmentation. Results show that the framework is effective, particularly when augmenting trajectory priorities and state-action q-targets, and it successfully learns from offline data, providing a practical alternative for settings where real-time BCI setups are impractical.
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