Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
A layered architecture for office delivery robots
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Numerical computation of rectangular bivariate and trivariate normal and t probabilities
Statistics and Computing
Focused real-time dynamic programming for MDPs: squeezing more out of a heuristic
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Active learning in partially observable markov decision processes
ECML'05 Proceedings of the 16th European conference on Machine Learning
The oz of wizard: simulating the human for interaction research
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
Decision making in assistive environments using multimodal observations
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Smoothed Sarsa: reinforcement learning for robot delivery tasks
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Hi-index | 0.00 |
This paper presents a reasoning system for a multi-modal service robot with human-robot interaction. The reasoning system uses partially observable Markov decision processes (POMDPs) for decision making and an intermediate level for bridging the gap of abstraction between multi-modal real world sensors and actuators on the one hand and POMDP reasoning on the other. A filter system handles the abstraction of multi-modal perception while preserving uncertainty and model-soundness. A command sequencer is utilized to control the execution of symbolic POMDP decisions on multiple actuator components. By using POMDP reasoning, the robot is able to deal with uncertainty in both observation and prediction of human behavior and can balance risk and opportunity. The system has been implemented on a multi-modal service robot and is able to let the robot act autonomously in modeled human-robot interaction scenarios. Experiments evaluate the characteristics of the proposed algorithms and architecture.