AI 中文总结
研究利用fNIRS脑信号调节机器人模拟学习的可行性，比较不同交互任务训练的智能体，测试多种增强强化学习算法的方法，发现该框架能有效利用神经信号增强学习，还可离线学习，为特定场景提供替代方案。
AI 中文摘要
人在回路强化学习已成为训练、微调机器人行为并使其与用户偏好一致的流行方法。本文探讨了通过功能近红外光谱（fNIRS）利用脑信号在模拟中调节机器人学习的可行性。比较了在被动（观察性）与主动（演示性）交互任务上训练的智能体，并测试了多种用神经信号增强强化学习算法的方法，重点是参数增强而非替换。还研究了模型粒度和噪声如何影响智能体学习。结果表明该框架有效，神经信号在增强轨迹优先级和状态 - 动作q值时可改善学习，且能从离线数据成功学习，为实时脑机接口设置不实用或数据有限的情况提供了实用替代方案。
英文摘要
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.
CommentsPreliminary results
