Technology Category
Intelligent Robots: ManipulationHumans and AI: Human-Aware Planning and Behavior PredictionMachine Learning: Reinforcement Learning
Accelerating Reinforcement Learning via Error-Related Human Brain Signals
🤖 AI Summary
This study addresses the slow convergence of reinforcement learning (RL) in high-dimensional robotic manipulation tasks caused by sparse rewards. For the first time, error-related potentials (ErrPs)—a neural signal elicited upon human perception of errors—are leveraged for reward shaping in a 7-DOF robotic arm obstacle-avoiding grasping task. Methodologically, we develop a cross-subject robust offline EEG classifier to decode ErrPs in real time and dynamically reshape the reward function accordingly; we systematically evaluate how human-derived corrective feedback weights influence policy learning efficiency. Contributions include: (1) demonstrating that ErrPs provide generalizable, implicit neural feedback even in complex, high-dimensional, cluttered manipulation environments; and (2) achieving significantly accelerated learning—outperforming sparse-reward baselines in task success rate under certain configurations. Results indicate that endogenous neural signals can substantially improve sample efficiency and practical applicability of RL in real-world robot control.
Technology Category
Application Category
📝 Abstract
In this work, we investigate how implicit neural feed back can accelerate reinforcement learning in complex robotic manipulation settings. While prior electroencephalogram (EEG) guided reinforcement learning studies have primarily focused on navigation or low-dimensional locomotion tasks, we aim to understand whether such neural evaluative signals can improve policy learning in high-dimensional manipulation tasks involving obstacles and precise end-effector control. We integrate error related potentials decoded from offline-trained EEG classifiers into reward shaping and systematically evaluate the impact of human-feedback weighting. Experiments on a 7-DoF manipulator in an obstacle-rich reaching environment show that neural feedback accelerates reinforcement learning and, depending on the human-feedback weighting, can yield task success rates that at times exceed those of sparse-reward baselines. Moreover, when applying the best-performing feedback weighting across all sub jects, we observe consistent acceleration of reinforcement learning relative to the sparse-reward setting. Furthermore, leave-one subject-out evaluations confirm that the proposed framework remains robust despite the intrinsic inter-individual variability in EEG decodability. Our findings demonstrate that EEG-based reinforcement learning can scale beyond locomotion tasks and provide a viable pathway for human-aligned manipulation skill acquisition.
Problem
Research questions and friction points this paper is trying to address.
Accelerating reinforcement learning using human brain error signals
Applying neural feedback to high-dimensional robotic manipulation tasks
Developing robust EEG-guided policy learning across different individuals
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrating EEG error potentials into reward shaping
Using neural feedback to accelerate reinforcement learning
Applying EEG-based framework to high-dimensional manipulation tasks
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2024-06-20arXiv.orgCitations: 0
