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Title: A Framework for Robot Grasp Transferring with Non-rigid Transformation
Grasp planning is essential for robots to execute dexterous tasks. Solving the optimal grasps for various objects online, however, is challenging due to the heavy computation load during exhaustive sampling, and the difficulties to consider task requirements. This paper proposes a framework to combine analytic approach with learning for efficient grasp generation. The example grasps are taught by human demonstration and mapped to similar objects by a non-rigid transformation. The mapped grasps are evaluated analytically and refined by an orientation search to improve the grasp robustness and robot reachability. The proposed approach is able to plan high-quality grasps, avoid collision, satisfy task requirements, and achieve efficient online planning. The effectiveness of the proposed method is verified by a series of experiments.  more » « less
Award ID(s):
1734109
NSF-PAR ID:
10213595
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Page Range / eLocation ID:
2941 to 2948
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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