3 papers
cs.RO2026
An offline approach to fNIRS-guided reinforcement learning for robot behavior
Julia Santaniello, Madelaine Brower, Benson Jiang +4
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 feasibili…
cs.AI2025
Towards Reinforcement Learning from Neural Feedback: Mapping fNIRS Signals to Agent Performance
Julia Santaniello, Matthew Russell, Benson Jiang +3
Reinforcement Learning from Human Feedback (RLHF) is a methodology that aligns agent behavior with human preferences by integrating user feedback into the agent's training process.…
cs.LG2025
Mapping Neural Signals to Agent Performance, A Step Towards Reinforcement Learning from Neural Feedback
Julia Santaniello, Matthew Russell, Benson Jiang +3
Implicit Human-in-the-Loop Reinforcement Learning (HITL-RL) is a methodology that integrates passive human feedback into autonomous agent training while minimizing human workload.…