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Active Visuo-Haptic Object Shape Completion
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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8
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IEEE Robotics and Automation Letters, Volume 7, issue 2, pp. 5254-5261
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 letter, 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 and that an uncertainty-driven haptic exploration policy leads to higher reconstruction accuracy than a random policy and a policy driven by Gaussian Process Implicit Surfaces. As a final experiment, we evaluate Act-VH and the best reconstruction baseline on grasping 10 novel objects. The results show that Act-VH reaches a significantly higher grasp success rate than the baseline on all objects. Together, this letter opens up the door for using active visuo-haptic shape completion in more complex cluttered scenes.
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Publisher Copyright: IEEE
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Rustler, L, Lundell, J, Behrens, J K, Kyrki, V & Hoffmann, M 2022, 'Active Visuo-Haptic Object Shape Completion', IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 5254-5261. https://doi.org/10.1109/LRA.2022.3152975