Object Recognition, Dynamic Contact Simulation, Detection, and Control of the Flexible Musculoskeletal Hand Using a Recurrent Neural Network with Parametric Bias

Abstract

The flexible musculoskeletal hand is difficult to modelize, and its model can change constantly due to deterioration over time, irreproducibility of initialization, etc. Also, for object recognition, contact detection, and contact control using the hand, it is desirable not to use a neural network trained for each task, but to use only one integrated network. Therefore, we develop a method to acquire a sensor state equation of the musculoskeletal hand using a recurrent neural network with parametric bias. By using this network, the hand can realize recognition of the grasped object, contact simulation, detection, and control, and can cope with deterioration over time, irreproducibility of initialization, etc. by updating parametric bias. We apply this study to the hand of the musculoskeletal humanoid Musashi and show its effectiveness.

Accepted at IEEE Robotics and Automation Letters

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• Deep Predictive Model Learning with Parametric Bias: Handling Modeling Difficulties and Temporal Model Changes
• Learning-Based Wiping Behavior of Low-Rigidity Robots Considering Various Surface Materials and Task Definitions
• Automatic Grouping of Redundant Sensors and Actuators Using Functional and Spatial Connections: Application to Muscle Grouping for Musculoskeletal Humanoids