The contributions in Toward Learning Robots address the question of how a robot can be designed to acquire autonomously whatever it needs to realize adequate behavior in a complex environment.
In-depth discussions of issues, techniques, and experiments in machine learning focus on improving ease of programming and enhancing robustness in unpredictable and changing environments, given limitations of time and resources available to researchers. The authors show practical progress toward a useful set of abstractions and techniques to describe and automate various aspects of learning in autonomous systems. The close interaction of such a system with the world reveals opportunities for new architectures and learning scenarios and for grounding symbolic representations, though such thorny problems as noise, choice of language, abstraction level of representation, and operationality have to be faced head-on.
Contents: Introduction: Toward Learning Robots. Learning Reliable Manipulation Strategies without Initial Physical Models. Learning by an Autonomous Agent in the Pushing Domain. A Cost-Sensitive Machine Learning Method for the Approach and Recognize Task. A Robot Exploration and Mapping Strategy Based on a Semantic Hierarchy of Spatial Representations. Understanding Object Motion: Recognition, Learning and Spatiotemporal Reasoning. Learning How to Plan. Robo-Soar: An Integration of External Interaction, Planning, and Learning Using Soar. Foundations of Learning in Autonomous Agents. Prior Knowledge and Autonomous Learning.