Volker Klingspor

Learning concepts from sensor data of a mobile robot

Machine learning can be a most valuable tool for improving the flexibility and efficiency of robot applications. Many approaches to applying machine learning to robotics are known. Some approaches enhance the robots high-level processing, the planning capabilities. Other approaches enhance the low-level processing, the control of basic actions. In contrast, the approach presented in this paper uses machine learning for enhancing the link between the low-level representations of sensing and action and the high-level representation of planning. The aim is to facilitate the communication between the robot and the human user. A hierarchy of concepts is learned from route records of a mobile robot. Perception and action are combined at every level, i.e., the concepts are perceptually anchored. The relational learning algorithm grdt has been developed which completely searches in a hypothesis space, that is restricted by rule schemata, which the user defines in terms of grammars.

Using machine learning techniques in real-world mobile robots

Applying machine learning techniques can help mobile robots meet the need for increased safety and adaptivity that real world operation demands. The techniques also facilitate robot to user communication. Using these techniques, we built increasingly abstract representations of a robots perceptions and actions. This produced a symbolic description of what the robot knows and can do. Because this task is fairly complex, we first identified those subproblems that a learning method can solve efficiently, and isolated those with good classical solutions. Also, for a robot to solve a complex problem, we had to find solutions for several learning tasks. We identified these learning tasks and the learning techniques appropriate for their solution. To evaluate our approach, we used the mobile robots Priamos and Teseo. >

Towards Concept Formation Grounded on Perception and Action of a Mobile Robot

The recognition of objects and, hence, their descriptions must be grounded in the environment in terms of sensor data. We argue why the concepts used to classify perceived objects and used to perform actions on these objects should integrate action-oriented perceptional features and perception-oriented action features. We present a grounded symbolic representation for these concepts. Moreover, the concepts should be learned. We show a logic-oriented approach to learning grounded concepts.

GRDT: enhancing model based learning for its application in robot navigation

One central point of machine learning in general and inductive logic programming in special is the search space of the algorithms, de ned by the control structure of the algorithms and additional knowledge. Since the sensible search space di ers from domain to domain, a exible way to describe this space is desired. To demonstrate problems occuring while using existing algorithms, we introduce learning tasks in a real world domain: concept learning for navigation of autonomous mobile robots. We point out di erences between three existing algorithms used within this framework and their results. Since all of these algorithms have problems in solving the tasks, we developed grdt (grammar based rule discovery tool), an algorithm combining their ideas and techniques. In grdt a two level description language is used for describing the hypothesis space. A grammar is used to de ne a set of second order rule schemata and these schemata then de ne the hypothesis space itself.

Learning Understandable Concepts for Robot Navigation

Service robots have to communicate with their human users in order to get commands, give reports, provide information, and get help in cases of failure. Service robots are not autonomous, but this does not mean that they can do without intelligence. They need intelligence in order to convert high-level human commands into their internal procedures and to adapt their execution to the actual environment. The concepts in commands and plans must be anchored in the “body” of the robot and at the same time be understandable to the human user. This means that robot and user must agree in a particular situation what a concept refers to, even though — because of their different sensory systems and action capabilities — the concept is defined completely different by robot and user. The concept definitions of the robot should include sensing and action so that the concepts become precise and specific when applied to a particular situation. We call such concepts operational concepts.

Program Optimization for Real-Time Performance

In this chapter, we present methods that allow a robot to use high-level knowledge for reasoning about its actions in real time. The knowledge is represented by a logic program that contains learned relational rules. In order to operationalize this knowledge we develop program optimization methods that speed up its evaluation. These are a program transformation method, a compilation method, and an efficient forward inference method. The latter is part of a performance system capable of inferring high-level concepts from the sensory input of a mobile robot on-line and in real time.

Human-agent interaction and machine learning

Human-Agent Interaction as a specific area of Human-Computer Interaction is of primary importance for the development of systems that should cooperate with humans. The ability to learn, i.e., to adapt to preferences, abilities and behaviour of a user and to peculiarities of the task at hand, should provide for both a wider range of application and a higher degree of acceptance of agent technology. In this paper, we discuss the role of Machine Learning as a basic technology for human-agent interaction and motivate the need for interdisciplinary approaches to solve problems related to communication with artificial agents for task specification, teaching, or information retrieval purposes.

Reactive Planning with Learned Operational Concepts

In the near future, services will be provided more and more by robots. The potential market for these service robots is expected to exceed that of current industrial robots until 2010 [1]. Currently, the first service robots are used in hospitals, factories, and to help the disabled [2]. Since users of service robots are usually no specialists in robots programming, new aspects for controlling robots should be topics of current research. The first topic we want to mention in this chapter is:

Learning In Human-Robot Communication

Human-Agent Interaction as a specific area of Human-Computer Interaction is of primary importance for the development of systems that should cooperate with humans. The ability to learn, i.e., to adapt to preferences, abilities and behaviour of a user and to the specifics of a given task, allows for both a wider range of application and a higher degree of acceptance of agent technology. Throughout this chapter, we will discuss the role of Machine Learning as a basic technology for human-agent interaction, and, especially, human-robot communication, according to the following categorization.