An offline approach to fNIRS-guided reinforcement learning for robot behavior
Human-in-the-loop Reinforcement Learning has become a popular approach to 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 (demonstrative) interaction tasks, and test multiple methods for enhancing the RL algorithm with the neural signal, focusing on paramete
Record details
Published: 15 July 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy
Retrieved: 18 July 2026
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ethics.ai (15 July 2026), “An offline approach to fNIRS-guided reinforcement learning for robot behavior,” evidence record 11369, https://ethics.ai/record/11369 (originally published by arXiv).
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