Mitsuhiro Hayashibe

Professor

Tohoku University

Japan

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Professor Mitsuhiro Hayashibe is a faculty member at Tohoku University, Japan. His research areas encompass robotics, motor control, and machine learning, with a strong focus on bimanual manipulation, gait generation, and human-robot interaction. Recent publications include works on tactile sensing in manipulation, gait strategies in stick insects, and collision avoidance for unmanned vehicles.

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2021

Collaborators (6)

Elijah Almanzor

University of Cambridge

UNITED KINGDOM

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Chatrin Phunruangsakao

Tohoku University

JAPAN

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Shin-ichi Izumi

Tohoku University graduate school of medicine

JAPAN

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Dai Owaki

Tohoku University

JAPAN

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Fumiya Iida

Reader

University of Cambridge

UNITED KINGDOM

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Saugat Bhattacharyya

Lecturer

University of Ulster

UNITED KINGDOM

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Mitsuhiro Hayashibe

Research Interests

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About

Articles (25)

Simultaneous estimation of contact position and tool shape with high-dimensional parameters using force measurements and particle filtering

TactileAloha: Learning Bimanual Manipulation With Tactile Sensing

Encoding flexible gait strategies in stick insects through data-driven inverse reinforcement learning

Concerted Control: Modulating Joint Stiffness Using GRF for Gait Generation At Different Speeds

Synergy-Based Evaluation of Hand Motor Function in Object Handling Using Virtual and Mixed Realities

A Systematic Review of Bimanual Motor Coordination in Brain-Computer Interface

Fixture-Free 2D Sewing Using a Dual-Arm Manipulator System

Local Collision Avoidance for Unmanned Surface Vehicles Based on an End-to-End Planner With a LiDAR Beam Map

Physics-Informed Learning Framework for Lower Limb Kinematic Prediction With Sparse Sensors and Its Application in Chronic Stroke

Two-Stage Learning of CPG and Postural Reflex Toward Quadruped Locomotion on Uneven Terrain With Simple Reward

Utilising redundancy in musculoskeletal systems for adaptive stiffness and muscle failure compensation: a model-free inverse statics approach

AI-CPG: Adaptive Imitated Central Pattern Generators for Bipedal Locomotion Learned Through Reinforced Reflex Neural Networks

Data-Driven Policy Learning Methods from Biological Behavior: A Systematic Review

Speed-Variable Gait Phase Estimation During Ambulation via Temporal Convolutional Network

Integrated Quantitative Evaluation of Spatial Cognition and Motor Function with HoloLens Mixed Reality

Transhumeral Arm Reaching Motion Prediction through Deep Reinforcement Learning-Based Synthetic Motion Cloning

A Survey of Sim-to-Real Transfer Techniques Applied to Reinforcement Learning for Bioinspired Robots

A Hierarchical Deep Reinforcement Learning Framework With High Efficiency and Generalization for Fast and Safe Navigation

Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion

Autonomous Navigation System in Pedestrian Scenarios Using a Dreamer-Based Motion Planner

Imitation Learning With Time-Varying Synergy for Compact Representation of Spatiotemporal Structures

LSTM Network-Based Estimation of Ground Reaction Forces During Walking in Stroke Patients Using Markerless Motion Capture System

Prediction of Whole-Body Velocity and Direction From Local Leg Joint Movements in Insect Walking via LSTM Neural Networks

Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

An Optimal Transport Based Transferable System for Detection of Erroneous Somato-Sensory Feedback from Neural Signals

Collaborators (6)

Elijah Almanzor

Chatrin Phunruangsakao

Shin-ichi Izumi

Dai Owaki

Fumiya Iida

Saugat Bhattacharyya

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