不確かさを手がかりに重心を探る：任意物体の重心推定のための能動知覚手法
Learning to Double Guess: An Active Perception Approach for Estimating the Center of Mass of Arbitrary Objects
ベイズニューラルネットで不確かさを定量化し、触覚を伴う複数回の情報量の多い把持・回転操作を能動的に選ぶことで、未知の実物体の重心を高精度に推定する枠組みを提案した。
著者: Shengmiao Jin, Yuchen Mo, Wenzhen Yuan
分類: cs.RO, cs.LG
原文アブストラクト
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.
