59,065 research outputs found
Affordance-Driven Next-Best-View Planning for Robotic Grasping
Grasping occluded objects in cluttered environments is an essential component
in complex robotic manipulation tasks. In this paper, we introduce an
AffordanCE-driven Next-Best-View planning policy (ACE-NBV) that tries to find a
feasible grasp for target object via continuously observing scenes from new
viewpoints. This policy is motivated by the observation that the grasp
affordances of an occluded object can be better-measured under the view when
the view-direction are the same as the grasp view. Specifically, our method
leverages the paradigm of novel view imagery to predict the grasps affordances
under previously unobserved view, and select next observation view based on the
highest imagined grasp quality of the target object. The experimental results
in simulation and on a real robot demonstrate the effectiveness of the proposed
affordance-driven next-best-view planning policy. Project page:
https://sszxc.net/ace-nbv/.Comment: Conference on Robot Learning (CoRL) 202
Fast Object Learning and Dual-arm Coordination for Cluttered Stowing, Picking, and Packing
Robotic picking from cluttered bins is a demanding task, for which Amazon
Robotics holds challenges. The 2017 Amazon Robotics Challenge (ARC) required
stowing items into a storage system, picking specific items, and packing them
into boxes. In this paper, we describe the entry of team NimbRo Picking. Our
deep object perception pipeline can be quickly and efficiently adapted to new
items using a custom turntable capture system and transfer learning. It
produces high-quality item segments, on which grasp poses are found. A planning
component coordinates manipulation actions between two robot arms, minimizing
execution time. The system has been demonstrated successfully at ARC, where our
team reached second places in both the picking task and the final stow-and-pick
task. We also evaluate individual components.Comment: In: Proceedings of the International Conference on Robotics and
Automation (ICRA) 201
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