This article describes the Multiagent Decision Process (MADP) Toolbox, a software library to support planning and learning for intelligent agents and multiagent systems in uncertain environments. Key features are that it supports partially observable environments and stochastic transition models; has unified support for single- and multiagent systems; provides a large number of models for decision-theoretic decision making, including one-shot and sequential decision making under various assumptions of observability and cooperation, such as Dec-POMDPs and POSGs; provides tools and parsers to quickly prototype new problems; provides an extensive range of planning and learning algorithms for single- and multiagent systems; is released under the GNU GPL v3 license; and is written in C++ and designed to be extensible via the object-oriented paradigm.

Vision systems are used more and more in 'personal' robots interacting with humans, since semantic information about objects and places can be derived from the rich sensory information. Visual information is also used for building appearance based topological maps, which can be used for localization. In this paper we describe a system capable of using this appearance based topological map for navigation. The system is made robust by using the epipolar geometry and a planar floor constraint in computing the necessary heading information. Using this method the robot is able to drive robustly in a large environment. We tested the method on real data under varying environment conditions and compared performance with a human-controlled robot.

An ambitious goal in modern robotic science is to build mobile robots that are able to interact as companions in real world environments. Especially for caretaking of elderly people a system robustly working at private homes is essential, requiring a very natural and human oriented way of communication. Since home environments are usually very individual a first task for a newly acquired robot is to get familiar with its new environment. This paper gives a short overview on how we integrated a vision based localization using the advantages of a very modular architecture and extending a spoken dialog system for online labeling and interaction about different locations. We present results from the integrated system working in a real, fully furnished home environment where it was able to learn the names of different rooms. This system enables us to perform real user studies in future without the need to fall back to Wizard-of-Oz experiments. Ongoing work aims at enabling the robot to take initiative by asking for unknown locations. A future extension is the ability to generalize over features of known rooms to make predictions when encountering unknown rooms

In this paper, we extend our appearance-based localization system moving from a passive approach to an active one where the robot can execute actions with the only purpose of gaining information about its location in the environment. We present a general framework for entropy-based action selection and we describe how this framework can be implemented in our localization system. The result is an action evaluation process more efficient in memory and in execution time than previously existing ones. The experiments we present show that the action selection mechanism effectively decreases the error on localization in environments with a high degree of aliasing. This can be of great help to improve the performance of our localization system in dynamic environments.

We apply the auxiliary particle filter algorithm of Pitt and Shephard (1999) to the problem of robot localization. To deal with the high-dimensional sensor observations (images) and an unknown observation model, we propose the use of an inverted nonparametric observation model computed by nearest neighbor conditional density estimation. We show that the proposed model can lead to a fully adapted optimal filter, and is able to successfully handle image occlusion and robot kidnap. The proposed algorithm is very simple to implement and exhibits a high degree of robustness in practice. We report experiments involving robot localization from omnidirectional vision in an indoor environment.