ArticleCommunications biology2021
Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials.
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 52 citations in OpenAlex.
- Non-Invasive Brain-Computer Interfaces: Converging Frontiers in Neural Signal Decoding and Flexible Bioelectronics Integration.Nano-micro letters · 2026Review
- Bio-Signal-Guided Robot Adaptive Stiffness Learning via Human-Teleoperated Demonstrations.Biomimetics (Basel, Switzerland) · 2025Article
- Yes or no? A study of ErrPs in the "guess what I am thinking" paradigm with stimuli of different visual content.Frontiers in psychology · 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
- Review
- EEG error-related potentials encode magnitude of errors and individual perceptual thresholds.iScience · 2023Article
- Body Extension by Using Two Mobile Manipulators.Cyborg and bionic systems (Washington, D.C.) · 2023Article
- Brain computer interface to distinguish between self and other related errors in human agent collaboration.Scientific reports · 2022Article
- [Research advances in non-invasive brain-computer interface control strategies].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- A Massage Area Positioning Algorithm for Intelligent Massage System.Computational intelligence and neuroscience · 2022Article
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Authors and funding
7 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Robotic assistance via motorized robotic arm manipulators can be of valuable assistance to individuals with upper-limb motor disabilities. Brain-computer interfaces (BCI) offer an intuitive means to control such assistive robotic manipulators. However, BCI performance may vary due to the non-stationary nature of the electroencephalogram (EEG) signals. It, hence, cannot be used safely for controlling tasks where errors may be detrimental to the user. Avoiding obstacles is one such task. As there exist many techniques to avoid obstacles in robotics, we propose to give the control to the robot to avoid obstacles and to leave to the user the choice of the robot behavior to do so a matter of personal preference as some users may be more daring while others more careful. We enable the users to train the robot controller to adapt its way to approach obstacles relying on BCI that detects error-related potentials (ErrP), indicative of the user's error expectation of the robot's current strategy to meet their preferences. Gaussian process-based inverse reinforcement learning, in combination with the ErrP-BCI, infers the user's preference and updates the obstacle avoidance controller so as to generate personalized robot trajectories. We validate the approach in experiments with thirteen able-bodied subjects using a robotic arm that picks up, places and avoids real-life objects. Results show that the algorithm can learn user's preference and adapt the robot behavior rapidly using less than five demonstrations not necessarily optimal.
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