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Having the ability to estimate an object&#x0027;s properties through interaction will enable robots to manipulate novel objects. Object&#x0027;s dynamics, specifically the friction and inertial parameters have only been estimated in a lab environment with precise and often external sensing. Could we infer an object&#x0027;s dynamics in the wild with only the robot&#x0027;s sensors? In this article, we explore the estimation of dynamics of a grasped object in motion, with tactile force sensing at multiple fingertips. Our estimation approach does not rely on torque sensing to estimate the dynamics. To estimate friction, we develop a control scheme to actively interact with the object until slip is detected. To robustly perform the inertial estimation, we setup a factor graph that fuses all our sensor measurements on physically consistent manifolds and perform inference. We show that tactile fingertips enable in-hand dynamics estimation of low mass objects.

In-Hand Object-Dynamics Inference Using Tactile Fingertips