Feasibility evaluation of object manipulation by a humanoid robot based on recursive estimation of the object's physical properties

Abstract

Whole-body manipulation is necessary for a humanoid robot to achieve tasks such as carrying large objects. One difficulty for achieving a whole-body manipulation is that the robot needs to select the appropriate operation from a list of candidates, such as lifting, pushing, and tilting. The appropriate operation depends upon the target object's physical properties, including its mass, center of mass, and friction coefficient, which are difficult to measure directly. In order to select the appropriate manipulation motion online, we propose a method of estimating the object's physical properties and evaluating the feasibility of the object operation. We calculate the likelihood of the object's physical properties from sensor information during manipulation and update these properties' probabifity distribution periodically based on Bayesian methods. The operational feasibility probability is evaluated by physics-based stability determination, allowing the robot to perform manipulation tasks by selecting the appropriate operation. We show the effectiveness of the proposed method by an experiment in which a life-sized humanoid robot carries a large object.