投稿日:

2 papers and 1 video were accepted for ICRA2023

  1. 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)
  2. 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)
  3. 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)
投稿日:

2 papers were accepted for ICRA2021

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.

  1. 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)
  2. 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)
投稿日:

3 papers for ICRA2020, 2 papers were journal option

3 papers have been accepted for ICRA2020 Paris!!

  1. 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!!
  2. 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!!
  3. 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!!
投稿日:

TVTS paper (Time-Varying and Time-Scalable Synergy) is accepted both for RAL journal and IROS2019!

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.