ArticleScientific reports2017
Intrinsic interactive reinforcement learning - Using error-related potentials for real world human-robot interaction.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed.
- Toward brain-trained humanoid robots.Frontiers in neurorobotics · 2026Article
- Error-related potentials detection to enhance human-robot collaboration: a mini review.Frontiers in neuroergonomics · 2026Review
- Human brain data: a valuable and open-ended resource for artificial intelligence.Frontiers in human neuroscience · 2026Article
- Cognitive state monitoring for neuroadaptive information visualization.Frontiers in human neuroscience · 2026Review
- Error-related potentials in EEG signals: feature-based detection for human-robot interaction.Scientific reports · 2025Article
- Hybrid brain-computer interface using error-related potential and reinforcement learning.Frontiers in human neuroscience · 2025Article
- Evaluating robotic actions: spatiotemporal brain dynamics of performance assessment in robot-assisted laparoscopic training.Frontiers in neuroergonomics · 2025Article
- Wearable high-density EMG sleeve for complex hand gesture classification and continuous joint angle estimation.Scientific reports · 2024Article
- Avoidance of specific calibration sessions in motor intention recognition for exoskeleton-supported rehabilitation through transfer learning on EEG data.Scientific reports · 2024Article
- A deep neural network and transfer learning combined method for cross-task classification of error-related potentials.Frontiers in human neuroscience · 2024Article
- Advancing passive BCIs: a feasibility study of two temporal derivative features and effect size-based feature selection in continuous online EEG-based machine error detection.Frontiers in neuroergonomics · 2024Article
- EEG and EMG dataset for the detection of errors introduced by an active orthosis device.Frontiers in human neuroscience · 2024Article
- Inferring individual evaluation criteria for reaching trajectories with obstacle avoidance from EEG signals.Scientific reports · 2023Article
- Bayesian learning from multi-way EEG feedback for robot navigation and target identification.Scientific reports · 2023Article
- Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Article
- Learning Rat-Like Behavioral Interaction Using a Small-Scale Robotic Rat.Cyborg and bionic systems (Washington, D.C.) · 2023Article
- Detection of tactile-based error-related potentials (ErrPs) in human-robot interaction.Frontiers in neurorobotics · 2023Article
- Combining brain-computer interfaces with deep reinforcement learning for robot training: a feasibility study in a simulation environment.Frontiers in neuroergonomics · 2023Article
- Brain computer interface to distinguish between self and other related errors in human agent collaboration.Scientific reports · 2022Article
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4 authors.
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Abstract
Reinforcement learning (RL) enables robots to learn its optimal behavioral strategy in dynamic environments based on feedback. Explicit human feedback during robot RL is advantageous, since an explicit reward function can be easily adapted. However, it is very demanding and tiresome for a human to continuously and explicitly generate feedback. Therefore, the development of implicit approaches is of high relevance. In this paper, we used an error-related potential (ErrP), an event-related activity in the human electroencephalogram (EEG), as an intrinsically generated implicit feedback (rewards) for RL. Initially we validated our approach with seven subjects in a simulated robot learning scenario. ErrPs were detected online in single trial with a balanced accuracy (bACC) of 91%, which was sufficient to learn to recognize gestures and the correct mapping between human gestures and robot actions in parallel. Finally, we validated our approach in a real robot scenario, in which seven subjects freely chose gestures and the real robot correctly learned the mapping between gestures and actions (ErrP detection (90% bACC)). In this paper, we demonstrated that intrinsically generated EEG-based human feedback in RL can successfully be used to implicitly improve gesture-based robot control during human-robot interaction. We call our approach intrinsic interactive RL.
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