For robotic systems to interact with objects in dynamic environments, it is
essential to perceive the physical properties of the objects such as shape,
friction coefficient, mass, center of mass, and inertia. This not only eases
selecting manipulation action but also ensures the task is performed as
desired. However, estimating the physical properties of especially novel
objects is a challenging problem, using either vision or tactile sensing. In
this work, we propose a novel framework to estimate key object parameters using
non-prehensile manipulation using vision and tactile sensing. Our proposed
active dual differentiable filtering (ADDF) approach as part of our framework
learns the object-robot interaction during non-prehensile object push to infer
the object's parameters. Our proposed method enables the robotic system to
employ vision and tactile information to interactively explore a novel object
via non-prehensile object push. The novel proposed N-step active formulation
within the differentiable filtering facilitates efficient learning of the
object-robot interaction model and during inference by selecting the next best
exploratory push actions (where to push? and how to push?). We extensively
evaluated our framework in simulation and real-robotic scenarios, yielding
superior performance to the state-of-the-art baseline.Comment: 8 pages. Accepted at IROS 202