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

• URL: http://arxiv.org/abs/2502.02663v1
• Date: Tue, 04 Feb 2025 19:03:21 GMT
• Title: Learning to Double Guess: An Active Perception Approach for Estimating the Center of Mass of Arbitrary Objects
• Authors: Shengmiao Jin, Yuchen Mo, Wenzhen Yuan,
• Abstract summary: We introduce 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. 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.
• Score: 9.790979989019952
• License:
• 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.

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