Grasping a Target Object in Clutter with an Anthropomorphic Robot Hand via RGB-D Vision Intelligence, Target Path Planning and Deep Reinforcement Learning


KIPS Transactions on Software and Data Engineering, Vol. 11, No. 9, pp. 363-370, Sep. 2022
https://doi.org/10.3745/KTSDE.2022.11.9.363,   PDF Download:  
Keywords: Anthropomorphic Robot Hand, Reinforcement Learning, Path Planning, Object Detection
Abstract

Grasping a target object among clutter objects without collision requires machine intelligence. Machine intelligence includes environment recognition, target & obstacle recognition, collision-free path planning, and object grasping intelligence of robot hands. In this work, we implement such system in simulation and hardware to grasp a target object without collision. We use a RGB-D image sensor to recognize the environment and objects. Various path-finding algorithms been implemented and tested to find collision-free paths. Finally for an anthropomorphic robot hand, object grasping intelligence is learned through deep reinforcement learning. In our simulation environment, grasping a target out of five clutter objects, showed an average success rate of 78.8%and a collision rate of 34% without path planning. Whereas our system combined with path planning showed an average success rate of 94% and an average collision rate of 20%. In our hardware environment grasping a target out of three clutter objects showed an average success rate of 30% and a collision rate of 97% without path planning whereas our system combined with path planning showed an average success rate of 90% and an average collision rate of 23%. Our results show that grasping a target object in clutter is feasible with vision intelligence, path planning, and deep RL.


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Cite this article
[IEEE Style]
G. H. Ryu, J. Oh, J. G. Jeong, H. Jung, J. H. Lee, P. R. Lopez, T. Kim, "Grasping a Target Object in Clutter with an Anthropomorphic Robot Hand via RGB-D Vision Intelligence, Target Path Planning and Deep Reinforcement Learning," KIPS Transactions on Software and Data Engineering, vol. 11, no. 9, pp. 363-370, 2022. DOI: https://doi.org/10.3745/KTSDE.2022.11.9.363.

[ACM Style]
Ga Hyeon Ryu, Ji-Heon Oh, Jin Gyun Jeong, Hwanseok Jung, Jin Hyuk Lee, Patricio Rivera Lopez, and Tae-Seong Kim. 2022. Grasping a Target Object in Clutter with an Anthropomorphic Robot Hand via RGB-D Vision Intelligence, Target Path Planning and Deep Reinforcement Learning. KIPS Transactions on Software and Data Engineering, 11, 9, (2022), 363-370. DOI: https://doi.org/10.3745/KTSDE.2022.11.9.363.