TL;DR
This paper introduces Act-VH, an active visuo-haptic shape completion method that improves object reconstruction accuracy by actively exploring uncertain regions with touch, enhancing robotic grasping in cluttered environments.
Contribution
The paper presents a novel active visuo-haptic shape completion approach using uncertainty-driven exploration, outperforming baselines in accuracy and grasp success rate.
Findings
Act-VH outperforms five baselines in simulation and real-world tests.
Uncertainty-driven exploration improves reconstruction accuracy.
Higher grasp success rates achieved with Act-VH on novel objects.
Abstract
Recent advancements in object shape completion have enabled impressive object reconstructions using only visual input. However, due to self-occlusion, the reconstructions have high uncertainty in the occluded object parts, which negatively impacts the performance of downstream robotic tasks such as grasping. In this work, we propose an active visuo-haptic shape completion method called Act-VH that actively computes where to touch the objects based on the reconstruction uncertainty. Act-VH reconstructs objects from point clouds and calculates the reconstruction uncertainty using IGR, a recent state-of-the-art implicit surface deep neural network. We experimentally evaluate the reconstruction accuracy of Act-VH against five baselines in simulation and in the real world. We also propose a new simulation environment for this purpose. The results show that Act-VH outperforms all baselines…
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Taxonomy
MethodsGaussian Process
