This paper describes Minerva, an interactive tour-guide robot that was successfully deployed in a Smithsonian museum. Minerva's software is pervasively probabilistic, relying on explicit representations of uncertainty in perception and control. This article describes

This paper presents a detailed study of average reward reinforcement learning, an undiscounted optimality framework that is more appropriate for cyclical tasks than the much better studied discounted framework. A wide spectrum of average reward algorithms are described, ranging from synchronous dynamic programming methods to several (provably convergent) asynchronous algorithms from optimal control and learning automata. A general sensitive discount optimality metric called n-discount-optimality is introduced, and used to compare the various algorithms. The overview identifies a key similarity across several asynchronous algorithms that is crucial to their convergence, namely independent estimation of the average reward and the relative values. The overview also uncovers a surprising limitation shared by the different algorithms: while several algorithms can provably generate gain-optimal policies that maximize average reward, none of them can reliably filter these to produce bias-optimal (or T-optimal) policies that also maximize the finite reward to absorbing goal states. This paper also presents a detailed empirical study of R-learning, an average reward reinforcement learning method, using two empirical testbeds: a stochastic grid world domain and a simulated robot environment. A detailed sensitivity analysis of R-learning is carried out to test its dependence on learning rates and exploration levels. The results suggest that R-learning is quite sensitive to exploration strategies, and can fall into sub-optimal limit cycles. The performance of R-learning is also compared with that of Q-learning, the best studied discounted RL method. Here, the results suggest that R-learning can be fine-tuned to give better performance than Q-learning in both domains.

The Nursebot project is a multi-disciplinary, multi-university effort aimed at developing mobile robotic assistants for the elderly.

This paper presents a novel method for creating high-fidelity models of animal behavior for use in robotic systems based on a behavioral systems approach, and describes in particular how an ethological model of a domestic dog can be implemented with AIBO, the Sony entertainment robot. I.

A theoretical and software framework is presented to facilitate the implementation of complex robotic tasks. Essential features of the framework are discussed, along with the actual implementation. To demonstrate the use of the framework, a controller for visually-guided door opening is implemented. This controller shows how a modular system can easily be designed and implemented using our framework. A discussion is also given, comparing this framework with other similar proposals. 1 Introduction The motivation for pursuing research in the area of intelligent service robots is many-fold. Intelligent service robots have a wide variety of potential applications, both at present and in the future. Obvious uses for autonomous robots are help for elderly and/or handicapped people, everyday chores like cleaning the oors, and fetching items (called fetch andcarry tasks). It is of key importance that these systems are easy to use and are safe. Installation should not require that an engi...

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