Learning to Double Guess: An Active Perception Approach for Estimating the Center of Mass of Arbitrary Objects
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915174859931648 |
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| author | Jin, Shengmiao Mo, Yuchen Yuan, Wenzhen |
| author_facet | Jin, Shengmiao Mo, Yuchen Yuan, Wenzhen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_02663 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Double Guess: An Active Perception Approach for Estimating the Center of Mass of Arbitrary Objects Jin, Shengmiao Mo, Yuchen Yuan, Wenzhen Robotics Machine Learning 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. |
| title | Learning to Double Guess: An Active Perception Approach for Estimating the Center of Mass of Arbitrary Objects |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2502.02663 |