# Accelerated Robot Learning via Human Brain Signals

arXiv: https://arxiv.org/abs/1910.00682 · HTML: https://mlsanity.com/paper/1910.00682/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

## Built on by (most cited first, 13 of 13)

- [Primitive Skill-based Robot Learning from Human Evaluative Feedback](https://mlsanity.com/paper/2307.15801/) (2023)
- [Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Tasks](https://mlsanity.com/paper/2107.05825/) (2021)
- [Interaction-Grounded Learning](https://mlsanity.com/paper/2106.04887/) (2021)
- [Maximizing BCI Human Feedback using Active Learning](https://mlsanity.com/paper/2008.04873/) (2020)
- [NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities](https://mlsanity.com/paper/2311.01454/) (2023)
- [Interaction-Grounded Learning with Action-inclusive Feedback](https://mlsanity.com/paper/2206.08364/) (2022)
- [Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback](https://mlsanity.com/paper/2507.13171/) (2025)
- [Towards Interactive Reinforcement Learning with Intrinsic Feedback](https://mlsanity.com/paper/2112.01575/) (2021)
- [Accelerating Reinforcement Learning via Error-Related Human Brain Signals](https://mlsanity.com/paper/2511.18878/) (2025)
- [Reinforcement Learning from Implicit Neural Feedback for Human-Aligned Robot Control](https://mlsanity.com/paper/2512.00050/) (2025)
- [Interaction-Grounded Learning for Contextual Markov Decision Processes with Personalized Feedback](https://mlsanity.com/paper/2602.08307/) (2026)
- [An offline approach to fNIRS-guided reinforcement learning for robot behavior](https://mlsanity.com/paper/2607.14393/) (2026)
- [Continuous Conditioning of VLAs with Augmenting EMG and Visual Task Descriptors](https://mlsanity.com/paper/2610.01794/) (2026)

## Abstract (arXiv)

In reinforcement learning (RL), sparse rewards are a natural way to specify the task to be learned. However, most RL algorithms struggle to learn in this setting since the learning signal is mostly zeros. In contrast, humans are good at assessing and predicting the future consequences of actions and can serve as good reward/policy shapers to accelerate the robot learning process. Previous works have shown that the human brain generates an error-related signal, measurable using electroencephelography (EEG), when the human perceives the task being done erroneously. In this work, we propose a method that uses evaluative feedback obtained from human brain signals measured via scalp EEG to accelerate RL for robotic agents in sparse reward settings. As the robot learns the task, the EEG of a human observer watching the robot attempts is recorded and decoded into noisy error feedback signal. From this feedback, we use supervised learning to obtain a policy that subsequently augments the behavior policy and guides exploration in the early stages of RL. This bootstraps the RL learning process to enable learning from sparse reward. Using a robotic navigation task as a test bed, we show that our method achieves a stable obstacle-avoidance policy with high success rate, outperforming learning from sparse rewards only that struggles to achieve obstacle avoidance behavior or fails to advance to the goal.