通过主动感知提升机器人对任意物体质心的估计精度
Learning to Double Guess: An Active Perception Approach for Estimating the Center of Mass of Arbitrary Objects
- 利用贝叶斯神经网络量化不确定性，指导多次交互探测
- 仅用少量训练数据即可在真实复杂物体上实现高精度估计
- 适合需要灵活抓取的机器人操作任务，尤其在传感器不准时
在非结构化环境中操控任意物体是机器人领域的重大挑战，主要源于难以准确确定物体的质心。本文提出U-GRAPH：一种基于不确定性的旋转主动感知框架，结合触觉信息以增强质心估计。传统方法依赖单次交互，受限于力矩传感器的固有误差。本方法通过贝叶斯神经网络（BNN）量化不确定性，并利用网格搜索与神经网络评分机制，引导机器人进行多轮信息丰富的交互。实验表明，该方法即使在小规模、低变异性的数据集上训练，仍能良好泛化至未见过的真实复杂物体，展现出优异的可迁移性。
原文摘要 · Abstract (English)
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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