基于fNIRS引导的机器人行为强化学习离线方法
An offline approach to fNIRS-guided reinforcement learning for robot behavior
Julia Santaniello, Madelaine Brower, Benson Jiang, Donatello Sassaroli, Chenyuan Zhang, Robert Jacob, Jivko Sinapov
AI 速读 基于摘要
作者探索用fNIRS脑信号在仿真中调制机器人强化学习，并比较被动观察与主动示范任务及多种信号融合方式。
cs.RO · cs.AI
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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 (demonstrative) interaction tasks, and test multiple methods for enhancing the RL algorithm with the neural signal, focusing on parameter augmentation in contrast to replacement. We further examine how model granularity and noise affect agent learning. Our results show that this framework is effective. The neural signal improves learning when augmenting trajectory priorities and state-action q-targets. Additionally, the framework learns successfully from offline data, offering a practical alternative for settings where real-time BCI setups are impractical or only limited data is available.