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For robotic systems to interact with objects in dynamic environments, it isessential to perceive the physical properties of the objects such as shape,friction coefficient, mass, center of mass, and inertia. This not only easesselecting manipulation action but also ensures the task is performed asdesired. However, estimating the physical properties of especially novelobjects is a challenging problem, using either vision or tactile sensing. Inthis work, we propose a novel framework to estimate key object parameters usingnon-prehensile manipulation using vision and tactile sensing. Our proposedactive dual differentiable filtering (ADDF) approach as part of our frameworklearns the object-robot interaction during non-prehensile object push to inferthe object's parameters. Our proposed method enables the robotic system toemploy vision and tactile information to interactively explore a novel objectvia non-prehensile object push. The novel proposed N-step active formulationwithin the differentiable filtering facilitates efficient learning of theobject-robot interaction model and during inference by selecting the next bestexploratory push actions (where to push? and how to push?). We extensivelyevaluated our framework in simulation and real-robotic scenarios, yieldingsuperior performance to the state-of-the-art baseline.

Push to know! -- Visuo-Tactile based Active Object Parameter Inference with Dual Differentiable Filtering