TY  - JOUR
T1  - Grasping a Target Object in Clutter with an Anthropomorphic Robot Hand via RGB-D Vision Intelligence, 
Target Path Planning and Deep Reinforcement Learning
AU  - Ryu, Ga Hyeon 
AU  - Oh, Ji-Heon 
AU  - Jeong, Jin Gyun 
AU  - Jung, Hwanseok 
AU  - Lee, Jin Hyuk 
AU  - Lopez, Patricio Rivera 
AU  - Kim, Tae-Seong 
JO  - KIPS Transactions on Software and Data Engineering
PY  - 2022
DA  - 2022/1/30
DO  - https://doi.org/10.3745/KTSDE.2022.11.9.363
KW  - Anthropomorphic Robot Hand
KW  - Reinforcement Learning
KW  - Path Planning
KW  - Object Detection
AB  - 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.