Countering murphys law: the use of anticipation and improvisation via an episodic memory in support of intelligent robot behavior

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/26466

Title: Countering murphys law: the use of anticipation and improvisation via an episodic memory in support of intelligent robot behavior
Author: Endo, Yoichiro
Abstract: Recently in robotics, substantial efforts have been invested on critical applications such as military, nursing, and search-and-rescue. These applications are critical in a sense that the robots may directly deal with human lives in life-or-death situations, and they are therefore required to make highly intelligent decisions as rapidly as possible. The intelligence we are looking for in this type of situations is proactiveness: the ability to anticipate as well as improvise. Anticipation here means that the robot can assess the current situation, predict the future consequence of the situation, and execute an action to have desired outcome based on the determined assessment and prediction. On the other hand, improvisation is performed when the consequence of the situation is not fully known. In other words, it is the ability to deal with a novel situation based on knowledge or skill being acquired before. In this presentation, we introduce a biologically inspired computational model of proactive intelligent behavior for robots. Integrating multiple levels of machine learning techniques such as temporal difference learning, instance-based learning, and partially observable Markov decision process, aggregated episodic memories are processed in order to accomplish anticipation as well as improvisation. How this model can be implemented within a software architectural framework and integrated into a physically realized robotic system is also explained. The experimental results using a real robot and high fidelity 3D simulators are then presented in order to help us understand how extended experience of a robot influences its ability to behave proactively.
Type: Dissertation
URI: http://hdl.handle.net/1853/26466
Date: 2008-10-21
Publisher: Georgia Institute of Technology
Subject: Somatic marker
POMDP
Improvisational robot
Anticipatory robot
Hippocampus
Episodic memory
Robotics
Artificial intelligence
Expectation (Psychology)
Machine learning
Markov processes
Department: Computing
Advisor: Committee Chair: Arkin, Ronald; Committee Member: Balch, Tucker; Committee Member: Dellaert, Frank; Committee Member: Potter, Steve; Committee Member: Ram, Ashwin
Degree: Ph.D.

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