Title :
A framework for cognitive robots to learn behaviors through imitation and interaction with humans
Author :
Tan, Huan ; Du, Qian ; Wu, Na
Author_Institution :
Electr. Eng. & Comput. Sci. Dept., Vanderbilt Univ., Nashville, TN, USA
Abstract :
This paper proposes a general learning framework for robots to learn behaviors through imitation and interaction. A modified codebook based method is used for robots to segment and recognize new objects in the environment. Task related semantic information is learned by robots through the speech communication with humans. Dynamic Movement Primitive method is used to generate similar behaviors to complete similar but slightly different tasks. Experimental results are given to verify the effectiveness of this framework.
Keywords :
cognitive systems; human-robot interaction; intelligent robots; learning (artificial intelligence); mobile robots; object recognition; robot vision; behavior learning framework; codebook based method; cognitive robots; dynamic movement primitive method; human-robot interaction; imitation learning; object recognition; object segmentation; speech communication; task related semantic information; Cognitive robotics; Humanoid robots; Humans; Learning systems; Semantics; USA Councils; Imitation learning; Interaction; Segmentation; Semantic Learning;
Conference_Titel :
Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), 2012 IEEE International Multi-Disciplinary Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
978-1-4673-0343-9
DOI :
10.1109/CogSIMA.2012.6188390
