DocumentCode
3507108
Title
Real-time imitation human upper-body movement with joint and self-collision constraints
Author
Chen Yihong ; Chen Qijun
Author_Institution
Sch. of Electron. & Inf., Tongji Univ., Shanghai, China
fYear
2013
fDate
12-15 Nov. 2013
Firstpage
1
Lastpage
6
Abstract
Imitation or programming by demonstration from humans is a kind of effective method for robots to learn human´s behaviors. This paper proposes a task-prior algorithm to solve the physical kinematic constraints of a full-body humanoid robot on real-time imitation. Redundant kinematic system is built to track the trajectory of the wrist position as a primary task. In the meanwhile, joint velocity limits and self-collision avoidance are introduced into the null space as additional tasks. Experiments on both simulation and a full-body humanoid are designed to verify the validity of the final policy.With the proposed method, the full-body humanoid robot succeeds in imitating humans´ movement with Kinect sensor and comparison between with and without considering the constraints are making to show the necessaries of this algorithm.
Keywords
collision avoidance; humanoid robots; image sensors; learning (artificial intelligence); robot kinematics; trajectory control; Kinect sensor; full-body humanoid robot; human movement; human upper-body movement; joint collision constraints; joint velocity limits; null space; physical kinematic constraints; programming-by-demonstration; realtime imitation; redundant kinematic system; robot learning; self-collision avoidance; self-collision constraints; task-prior algorithm; wrist position trajectory; Collision avoidance; Humanoid robots; Joints; Kinematics; Robot kinematics; Trajectory; Imitation Joint limit Self-collision Task-prior Null space;
fLanguage
English
Publisher
ieee
Conference_Titel
Robotics, Automation and Mechatronics (RAM), 2013 6th IEEE Conference on
Conference_Location
Manila
ISSN
2158-2181
Print_ISBN
978-1-4799-1198-1
Type
conf
DOI
10.1109/RAM.2013.6758550
Filename
6758550
Link To Document