Procedural Memory Learning from Demonstration for Task Performance

• Published in: 2015 IEEE International Conference on Systems, Man, and Cybernetics pp. 2435 - 2440
• Main Authors: Yong-Ho Yoo, Jong-Hwan Kim
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2015
• Subjects: Adaptation models; Data models; Feature extraction; Knowledge based systems; Learning from demonstration; procedural memory; robot learning; Robots; Support vector machines; Three-dimensional displays
• DOI: 10.1109/SMC.2015.426

A robot is expected to carry out a task autonomously with its own knowledge system. Using the knowledge system, the robot can recognize current situation and recall a proper sequence for performing an appropriate task in that situation. To build such knowledge system, the robot learns the knowledge from user demonstrations as if a child learns through interactions with parents and teachers. User demonstration is captured by an RGBD camera embedded the robot. The robot needs to segment each execution from continuous RGB-D streams. In this paper, each execution is composed of an object and an action performed on the object. The sequence of executions, or the procedure, should be stored in the robot's memory for the the robot to retrieve and execute the procedure in a similar situation later. Such a procedural memory is developed based on an adaptive resonance system. Using the procedural memory learned, the robot can perform the full sequences of tasks with only partial information given on executions. The effectiveness of the proposed scheme is demonstrated for four tasks through computer simulations.