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.

arXiv commentAccepted at IEEE Robotics and Automation Letters

References in corpus (7)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Component Modularized Design of Musculoskeletal Humanoid Platform Musashi to Investigate Learning Control Systems
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• Environmentally Adaptive Control Including Variance Minimization Using Stochastic Predictive Network with Parametric Bias: Application to Mobile Robots
• Learning of Balance Controller Considering Changes in Body State for Musculoskeletal Humanoids
• Learning-Based Wiping Behavior of Low-Rigidity Robots Considering Various Surface Materials and Task Definitions
• Deep Predictive Model Learning with Parametric Bias: Handling Modeling Difficulties and Temporal Model Changes
• Automatic Grouping of Redundant Sensors and Actuators Using Functional and Spatial Connections: Application to Muscle Grouping for Musculoskeletal Humanoids
• Characteristics, Management, and Utilization of Muscles in Musculoskeletal Humanoids: Empirical Study on Kengoro and Musashi