Closed-Loop Next-Best-View Planning for Target-Driven Grasping
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Author / Producer
Date
2022
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
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Abstract
Picking a specific object from clutter is an essential component of many manipulation tasks. Partial observations often require the robot to collect additional views of the scene before attempting a grasp. This paper proposes a closed-loop next-best-view planner that drives exploration based on occluded object parts. By continuously predicting grasps from an up-to-date scene reconstruction, our policy can decide online to finalize a grasp execution or to adapt the robot's trajectory for further exploration. We show that our reactive approach decreases execution times without loss of grasp success rates compared to common camera placements and handles situations where the fixed baselines fail. Video and code are available at https://github.com/ethz-asl/active_grasp.
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Publication status
published
Editor
Book title
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)
Journal / series
Volume
Pages / Article No.
1411 - 1416
Publisher
IEEE
Event
35th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Robot vision systems; Grasping; Cameras; Search problems; Robustness; Trajectory; Planning
Organisational unit
03737 - Siegwart, Roland Y. / Siegwart, Roland Y.