mlsanityData to 14 Oct 2026

An offline approach to fNIRS-guided reinforcement learning for robot behavior

arXiv 2607.14393 ↗First posted 15 Jul 2026cs.RO cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 24 references that are in the map. First posted on arXiv 15 Jul 2026.

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References · 14

  1. 2017Hindsight Experience Replaycited by 1,136
  2. 2015Prioritized Experience Replaycited by 1,099
  3. 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 759
  4. 2017Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spacescited by 123
  5. 2017Trial without Error: Towards Safe Reinforcement Learning via Human Interventioncited by 103
  6. 2018DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedbackcited by 41
  7. 2020The EMPATHIC Framework for Task Learning from Implicit Human Feedbackcited by 38
  8. 2020Modeling and mitigating human annotation errors to design efficient stream processing systems with human-in-the-loop machine learningcited by 21
  9. 2019Accelerated Robot Learning via Human Brain Signalscited by 13
  10. 2020Maximizing BCI Human Feedback using Active Learningcited by 8
  11. 2025Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedbackcited by 3
  12. 2021Towards Interactive Reinforcement Learning with Intrinsic Feedbackcited by 2
2 more
  1. 2025Mapping Neural Signals to Agent Performance, A Step Towards Reinforcement Learning from Neural Feedbackcited by 1
  2. 2025Towards Reinforcement Learning from Neural Feedback: Mapping fNIRS Signals to Agent Performancecited by 1

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  3. 2025RFTF: Reinforcement Fine-tuning for Embodied Agents with Temporal Feedback
  4. 2024Deep Dive into Model-free Reinforcement Learning for Biological and Robotic Systems: Theory and Practice
  5. 2026The AI Neuroscientist: An Interactive Agentic Interface for Neuroimaging Analysis
  6. 2023Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning
  7. 2025Spectral Representation-based Reinforcement Learning
  8. 2024Fourier Controller Networks for Real-Time Decision-Making in Embodied Learning