Learning to Double Guess: An Active Perception Approach for Estimating the Center of Mass of Arbitrary Objects

Abstract-- Manipulating arbitrary objects in unstructured environments is a significant challenge in robotics, primarily due to difficulties in determining an object's center of mass. This paper introduces U-GRAPH: Uncertainty-Guided Rotational Active Perception with Haptics, a novel framework to enhance the center of mass estimation using active perception. Traditional methods often rely on single interaction and are limited by the inherent inaccuracies of Force-Torque (F/T) sensors. Our approach circumvents these limitations by integrating a Bayesian Neural Network (BNN) to quantify uncertainty and guide the robotic system through multiple, information-rich interactions via grid search and a neural network that scores each action. We demonstrate the remarkable generalizability and transferability of our method with training on a small dataset with limited variation yet still perform well on unseen complex real-world objects. With the growing interest in robotics manipulation in the wild, researchers have been investigating ways for robots to Figure 1: We design an active perception algorithm to estimate interact with different objects. Our algorithm uses the secure grasp is the proximity of the grasp point to the object's first estimation from the F/T reading to infer a new rotational center of mass (CoM).