Welcome to my page!

My name is Masashi Hamaya (濵屋 政志). I am a principal investigator at OMRON SINIC X Corporation. I obtained a Ph.D. degree  (Eng.) at Osaka University in 2019. My current research is controlling soft robots for assemblies.

Research interests: Soft Robotics, Exoskeletons, Human-Robot Interaction, Machine Learning, and Reinforcement Learning

Contact: masashi.hamaya [at] sinicx.com

Last update: 11th May 2024




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Research projects

Design of physical user–robot interactions for model identification of soft actuators on exoskeleton robots , IJRR 2019

Recent breakthroughs in wearable robots, such as exoskeleton robots with soft actuators and soft exosuits, have enabled the use of safe and comfortable movement assistance. However, modeling and identification methods for soft actuators used in wearable robots have yet to be sufficiently explored. In this study, we propose a novel approach for obtaining accurate soft actuator models through the design of physical user–robot interactions for wearable robots, in which the user applies external forces to the robot. To obtain an accurate soft actuator model from the limited amount of data acquired through an interaction, we leverage an active learning framework based on Gaussian process regression. We conducted experiments using a two-degree-of-freedom upper-limb exoskeleton robot with four pneumatic artificial muscles (PAMs). Experimental results showed that physical interactions between the exoskeleton robot and the user were successfully designed to allow PAM models to be identified. Furthermore, we found that data acquired through an interaction could result in more accurate soft actuator models for the exoskeleton robots than data acquired without a physical interaction between the exoskeleton robot and the user. 

Exploiting Human and Robot Muscle Synergies for Human-in-the-loop Optimization of EMG-based Assistive Strategies, ICRA 2019, IEEE Robotics and Automation Society Japan Joint Chapter Young Award

In this study, we propose a novel human-in-the-loop optimization approach for exoskeleton robot control. We develop a method to optimize widely-used Electromyography (EMG)-based assistive strategies. If we use multiple EMG channels to control multi-DoF robots, optimization process becomes complex and requires a large amount of data. To make the optimization tractable, we exploit the synergies both of the human muscles and artificial muscles of the exoskeleton robots to reduce the number of parameters of the assistive strategies. We show that we can extract the synergies not only from the user’s muscle activities but from pneumatic artificial muscle (PAMs) contractions of the exoskeleton robot.

Then, we adopt a Bayesian optimization method to acquire the parameters for assisting human movements by iteratively identifying the user’s preferences of the assistive strategies. We conducted experiments to evaluate our proposed method with a PAMs-driven upper-limb exoskeleton robot. Our method successfully learned assistive strategies from the human-in-the-loop optimization with a practicable number of interactions.

Learning assistive strategies for exoskeleton robots from user-robot physical interaction, Pattern Recognit. Lett. 2017

With exoskeleton robots, an assistive strategy is a key ingredient. Since interactions between users and exoskeleton robots are bidirectional, the assistive strategy design problem is complex and challenging. We explore a data-driven learning approach for designing assistive strategies for exoskeletons from user-robot physical interaction. We formulate the learning problem of assistive strategies as a policy search problem and exploit a data-efficient model-based reinforcement learning framework. Instead of explicitly providing the desired trajectories in the cost function, our cost function only considers the user’s muscular effort measured by electromyography signals (EMGs) to learn the assistive strategies.  We applied our method to a 1-DoF exoskeleton robot and conducted a series of experiments with human subjects. Our experimental results demonstrated that our method learned proper assistive strategies that explicitly considered the bidirectional interactions between a user and a robot with only 60 seconds of interaction. 

Pneumatic Artificial Muscle (PAM) identification for exoskeleton robots by user-root collaboration, IROS 2017

Pneumatic Artificial Muscle (PAM) actuators have been used as exoskeletons because of their inherited compliance and high power-weight ratio. However, creating accurate models remains difficult mainly due to the compliance issue; the model can be changed by the force applied by the user. Therefore, both user and robot actions need to be considered for sufficient excitation of PAMs that are equipped in exoskeleton robots, unlike typical rigid actuators that can only be sufficiently excited by robot actions.  We propose a user-robot collaborative excitation approach for PAM model identification as an active learning framework for sequentially collecting data by deriving and executing optimal user and robot actions at each step with Gaussian processes . Experimental results show that our method can more efficiently identify the PAM model than a standard model identification method that does not use any data acquired through user-robot collaboration.