Learning-based object's stiffness and shape estimation with confidence level in multi-fingered hand grasping.

ABSTRACT:

Introduction

When humans grasp an object, they are capable of recognizing its characteristics, such as its stiffness and shape, through the sensation of their hands. They can also determine their level of confidence in the estimated object properties. In this study, we developed a method for multi-fingered hands to estimate both physical and geometric properties, such as the stiffness and shape of an object. Their confidence levels were measured using proprioceptive signals, such as joint angles and velocity.

Method

We have developed a learning framework based on probabilistic inference that does not necessitate hyperparameters to maintain equilibrium between the estimation of diverse types of properties. Using this framework, we have implemented recurrent neural networks th

SUBMITTER: Kutsuzawa K 

PROVIDER: S-EPMC11611863 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature