The flexible under-actuated musculoskeletal hand is superior in its adaptability and impact resistance. On the other hand, since the relationship between sensors and actuators cannot be uniquely determined, almost all its controls are based on feedforward controls. When grasping and using a tool, the contact state of the hand gradually changes due to the inertia of the tool or impact of action, and the initial contact state is hardly kept. In this study, we propose a system that trains the predictive network of sensor state transition using the actual robot sensor information, and keeps the initial contact state by a feedback control using the network. We conduct experiments of hammer hitting, vacuuming, and brooming, and verify the effectiveness of this study.
Object Recognition, Dynamic Contact Simulation, Detection, and Control of the Flexible Musculoskeletal Hand Using a Recurrent Neural Network with Parametric Bias
The University of Tokyo（东京大学）；TOYOTA MOTOR CORPORATION（丰田汽车公司）
High-Power, Flexible, Robust Hand: Development of Musculoskeletal Hand Using Machined Springs and Realization of Self-Weight Supporting Motion with Humanoid
Department of Mechanical Engineering（机械工程系）；Carnegie Mellon University（卡内基梅隆大学）；Department of Physical Education（体育系）；Seoul National University（首尔国立大学）；Lampe Joint Department of Biomedical Engineering（生物医学工程联合部门）；UNC-Chapel Hill and NC State University（北卡罗来纳大学教堂山分校和北卡罗来纳州立大学）
Lambda-Hold Control: Human-Like Movement Emerges from a Minimal Task Reward in Predictive Musculoskeletal Simulation
Institute of Sport Science, Seoul National University（首尔国立大学体育科学研究所）；SNU Robotics Institute, Seoul National University（首尔国立大学SNU机器人研究所）；Department of Physical Education, Seoul National University（首尔国立大学体育学院）
Predictive Modeling of Flexible EHD Pumps using Kolmogorov-Arnold Networks
College of Mechanical Engineering, Chongqing University of Technology（重庆理工大学机械工程学院）；Nagoya University（名古屋大学）；Faculty of Engineering, Yamaguchi University（山口大学工程学院）
Environmentally Adaptive Control Including Variance Minimization Using Stochastic Predictive Network with Parametric Bias: Application to Mobile Robots
Scalable predictive processing framework for multitask caregiving robots
National Institute of Neuroscience, National Center of Neurology and Psychiatry（国立精神·神经医疗研究中心 神经科学研究所）；Future Robotics Organization, Waseda University（早稻田大学未来机器人机构）；Department of Intermedia Art and Science, Waseda University（早稻田大学跨媒体艺术与科学系）
