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

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.

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 that estimate the stiffness and shape of grasped objects with their uncertainty in real time.

We demonstrated that the trained neural networks are capable of representing the confidence level of estimation that includes the degree of uncertainty and task difficulty in the form of variance and entropy.

We believe that this approach will contribute to reliable state estimation. Our approach would also be able to combine with flexible object manipulation and probabilistic inference-based decision making.