- Motor Synergy Development in Symmetric Gait of Whole-body Locomotion Learning, IEEE Int. Conf. on Robotics and Automation, May 2023, G. Li, A. d’Avella, M. Hayashibe (ICRA2023Video)
- Learnable Tegotae-based Feedback in CPGs with Sparse Observation Produces Efficient and Adaptive Locomotion, IEEE Int. Conf. on Robotics and Automation, May 2023, C. Herneth, M. Hayashibe, D. Owaki (ICRA2023)
- Morphological Characteristics That Enable Stable and Efficient Walking in Hexapod Robot Driven by Reflex-based Intra-limb Coordination, IEEE Int. Conf. on Robotics and Automation, May 2023, W. Sato, J. Nishii, M. Hayashibe, D. Owaki (ICRA2023)
2 papers have been accepted for ICRA2021!
1st one is about synergy-redundancy coupling, and its usage for quantification of redundancy considering given task and dynamic feasibility through deep learning.
2nd one is about softrobotics swim simulation and its deep learning framework.
- Quantification of Joint Redundancy considering Dynamic Feasibility using Deep Reinforcement Learning, IEEE Int. Conf. on Robotics and Automation, June 2021, J. Chai, M. Hayashibe (ICRA2021)
- Deep Reinforcement Learning Framework for Underwater Locomotion of Soft Robot, IEEE Int. Conf. on Robotics and Automation, June 2021, L. Guanda, J. Shintake, M. Hayashibe (ICRA2021)
3 papers have been accepted for ICRA2020 Paris!!
- Simultaneous On-line Motion Discrimination and Evaluation of Whole-body Exercise by Synergy Probes for Home Rehabilitation, IEEE Int. Conf. on Robotics and Automation, (2020), (accepted), Felipe M. Ramos, M. Hayashibe, (ICRA2020 Paris) This paper is important work for On-line Synergy Tracking which allows simultaneous motion discrimination and motion evaluation with home setting!!
- Motor Synergy Development in High-performing Deep Reinforcement Learning algorithms, IEEE Robotics and Automation Letters, (2020),(accepted), Jiazheng Chai, M. Hayashibe, (ICRA2020 Paris) This paper is important work for revealing what is going on under deep reinforcement learning in motion learning. Spatio-temporal synergy is emerged in the course of learning without specifying any synergy usage!!
- Discovering Interpretable Dynamics by Sparsity Promotion on Energy and the Lagrangian, IEEE Robotics and Automation Letters, (2020), (accepted), Hoang K. Chu, M. Hayashibe, (ICRA2020 Paris) This paper is important work for discovering analytical dynamics with experimental observation. Sparse Analytical models for unknown dynamics could be obtained from observation of mass motion with white-box approach!!
Identification of Time-Varying and Time-Scalable Synergies From Continuous Electromyographic Patterns, IEEE Robotics and Automation Letters, (2019), Felipe M. Ramos, Andrea d’Avella, Mitsuhiro Hayashibe, (accepted both for RAL journal and IROS2019 Macau)
Existing Time-Varying synergy may detect different speed motions as different synergy combination as time component is different between slow and fast movement. However, in neuroscience perspective, it should come from common synergy just with different speed control. The proposed method allows to make new analysis for auto-identify the common synergy over different speed motions during the synergy extraction.
I have attended and presented our activity for Indo-French Workshop on Robotics for Rehabilitation at Chandigarh, India, February 26-28, 2019. It was nice event to find network both in India and France.
I was selected for Toyota Riken Scholar
第１回バイオメカニクス運動制御に関するアジアシンポジウム２０１８年４月２０日 | 東京工業大学
The De Luca Foundation, USA is pleased to announce that the 15th Annual Delsys Prize for Innovation in Electromyography was awarded to Dr. Mitsuhiro Hayashibe of Tohoku University, Japan.
Dr. Hayashibe’s winning proposal, titled “Evoked Electromyographically Controlled Electrical Stimulation”, was selected from a field of 135 entries from 30 countries. The entries represented a remarkably broad range of interests in diverse areas such as Biomechanics, Exercise Physiology, Signal Processing, Facial EMG, Robotics, Rehabilitation, and various others.
It is heartening to witness the growth and continuing maturation of the field of Electromyography.
We are honored to play a small part in this success story by illuminating outstanding contributions to the field.
We would like to thank the Delsys Prize Review Board Members: Dr. Andrea d’Avella (Italy), Dr. Kevin Englehart (Canada), Dr. Jim Richards (UK), and Dr. Helen Huang (USA), for taking part in choosing the winner.
Delsys Inc., https://www.delsys.com/
De Luca Foundation, https://www.delucafoundation.org/
Delsys Prize Winners, https://www.delucafoundation.org/activities/honor-research/delsys-prize/winners/
Prof. Hayashibe is First Japanese Recipient for this international EMG award.
工学研究科ロボティクス専攻の林部充宏教授が、De Luca FoundationよりThe 15th Annual Delsys Prize for Innovation in Electromyographyを受賞しました。 この賞は、米国De Luca Foundationが主催するEMG（Electromyography）技術のInnovationに関する国際的な賞で、日本人としては初めての受賞となります。
今回、「Evoked Electromyographically Controlled Electrical Stimulation」が評価されての受賞です。脊髄損傷患者において電気刺激により誘起されたEMG信号を利用して筋肉活動度の直接的制御を可能とした制御技術に関する研究です。
Dr. Hayashibe gives seminar on Neuro-Robotics at AOS-Fall 2017.
University of Washington – Tohoku University Academic Open Space
Petersen Room, Allen Library
University of Washington, Seattel, WA
15-17 November, 2017