用脑电信号指导机器人学习，离线训练也有效。
An offline approach to fNIRS-guided reinforcement learning for robot behavior
- 用fNIRS脑信号增强动作优先级和目标值，不替换原有模型
- 离线数据下仍能提升学习效果，成功率高于基准37%
- 适合实时脑机接口不可行的场景，如医疗或远程控制
人机协同强化学习已成为训练、微调和对齐机器人行为与用户偏好的主流方法。本文探索利用功能近红外光谱（fNIRS）获取的脑信号，在仿真环境中调节机器人学习过程的可行性。比较了被动（观察式）与主动（示范式）交互任务下训练的智能体表现，并测试多种将神经信号融入强化学习算法的方法，重点采用参数增强而非替换策略。进一步研究模型粒度与噪声对学习的影响。结果表明，该框架有效：当在轨迹优先级和状态-动作Q目标中融合脑信号时，学习性能显著提升。此外，该方法可在离线数据上成功训练，为实时脑机接口不适用或仅有限数据可用的场景提供了实用替代方案。
原文摘要 · Abstract (English)
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.
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