Holger Friedrich

Interactive Robot Programming Based on Human Demonstration and Advice

Service robots require interactive programming interfaces that allow users without programming experience to easily instruct the robots. Systems following the Programming by Demonstration (PbD) paradigm that were developed within the last years are getting closer to this goal. However, most of these systems lack the possibility for the user to supervise and influence the process of program generation after the initial demonstration was performed. In this paper an approach is presented, that enables the user to supervise the entire program generation process and to annotate, and edit system hypotheses. Moreover, the knowledge representation and algorithms presented enable the user to generalise the generated program by annotating conditions and object selection criteria via a 3D simulation and graphical user interface. The resulting PbD-system widens the PbD approach in robotics to programming based on human demonstrations and user annotations.

Interactive generation of flexible robot programs

Service robots require interactive programming interfaces that allow users without programming experience to easily instruct the robots. Systems following the programming by demonstration (PbD) paradigm that were developed within the last years are getting closer to this goal. However, most of these systems lack the possibility for the user to supervise and alter the course of program generation after the initial demonstration was performed. In this paper we present an approach, where the user is able to supervise the entire program generation process and to annotate, and edit system hypotheses. Moreover, the knowledge representation and algorithms presented enable the user to generalize the generated program by annotating conditions and object selection criteria via a 3D simulation and graphical user interface. The resulting PbD-system widens the PbD approach in robotics to the interactive generation of flexible robot programs based on demonstration and annotations.

Learning and Communication in Multi-Agent Systems

This paper discusses the significance of communication between individual agents that are embedded into learning Multi-Agent Systems. For several learning tasks occurring within a Multi-Agent System, communication activities are investigated and the need for a mutual understanding of agents participating in the learning process is made explicit. Thus, the need for a common ontology to exchange learning-related information is shown. Building this ontology is an additional learning task that is not only extremely important, but also extremely difficult. We propose a. solution that is motivated by the human ability to understand each other even in the absence of a common language by using alternative communication channels, such as gestures. We show some results for the task of cooperative material handling by several manipulators.

What Can Robots Learn from Humans

Abstract Programming by Demonstration (PbD) is an intuitive method to program a robot. The user, acting as a teacher or programmer, shows how a particular task should be carried out. The demonstration is monitored using an interface device that allows the measurement and recording of both the applied commands as well as the data simultaneously perceived by robot’s sensors. This paper identifies the kind of knowledge that the robot can actually acquire from the human user through demonstrations and the requirements that must be met in order to be able to interpret what has been demonstrated. Finally, it presents and experimentally evaluates an approach to integrated acquisition, evaluation, tuning, and execution of elementary skills and task-level programs for robots based on human demonstrations.

Integrating skills into multi-agent systems

Currently, an important topic of robotic research is the design and development of multi-agent robot systems (MASs). In these a number of autonomous robots cooperate and coordinate themselves in order to pursue given goals. The agents of an MAS not only have to work autonomously or in cooperation with other agents, but in dynamic, relatively unstructured environments. Therefore, the agents require agent-specific but flexible skills to cope with their tasks and the environments variability. On the other hand, the actions to be performed by agents in an MAS have to meet certain requirements imposed by the MASs structure. The representation of actions has to support planning, inter-agent communication, task negotiation etc. In this paper, we describe a method of combining the agent-specific nature of skills with the requirements for a general action knowledge representation inherent to MASs, by presenting elementary operations (EOs) that provide an appropriate interface.

Learning coordination skills in multi-agent systems

While distributed control architectures have many advantages over centralized ones, such as their inherent modularity and fault tolerance, a major problem of such architectures is to ensure the goal-oriented behaviour of the controlled system. This paper presents a framework within which the coordination skills required for goal-orientedness are learned from user demonstrations. The framework is based on a state-space model of the single agents building the system and a corresponding model of the coordination mechanism. Our mobile robot PRIAMOS provides an application example.

Integration of Symbolic and Subsymbolic Learning to Support Robot Programming by Human Demonstration

One of the major cost factors related to robotic applications is the development of robot programs. Especially the use of multi sensor systems and the demand for various skills requires experienced programmers and efficient, programming environments. Such sophisticated programming systems are usually available in industrial environments. However, they do not exist for robot applications in a public or personal environment. To enable new robot applications with emphasis on service tasks [40], it is necessary to develop techniques which allow untrained users to program a service robot quickly, safely, and efficiently.

3D-icon based user interaction for robot programming by demonstration

Within the last years the programming by demonstration (PbD) programming methodology gained more and more attention in robotics. However, a high quality way of human-robot interaction is crucial for a successful application of the PbD methodology in robotics. Hypotheses derived from the programming system, and control knowledge included in a generated robot program has to be checked back with and to be verified by the user in order to avoid potentially harmful errors that lead to faulty code. This paper describes a method for user-robot interaction that is based on 3D-icons and supports and facilitates the programming process in a robot programming by demonstration system significantly.

Communication and propagation of action knowledge in multi-agent systems

Abstract Multi-agent systems (MASs) do play an important role in the construction of fault tolerant and robust robot systems. One major advantage of MAS is the fact that multiple agents work towards a common goal, having different skills for specific subtasks. Usually, agents have to use a common description of the actions to be carried out. Since agents can join and leave the MAS at any time, it is important that knowledge acquired by single agents can be transferred or propagated between agents, to ensure that knowledge is not lost, in the case that an agent leaves the system. In this paper, techniques will be presented that enable representation of common extendable action knowledge for task solutions in an agent’s knowledge base and additionally, algorithms for propagating this knowledge between agents efficiently and with minimum required communication effort.

Programming by Demonstration: A Machine Learning Approach to Support Skill Acquisiton for Robots

Programming by Demonstration (PbD) is a programming method that allows to add new functionalities to a system by simply showing the desired task or skill in form of few examples. In the domain of robotics this paradigm offers the potential to reduce the complexity of robot task programming and to make programming more ”natural”. In case of programming an assembly task PbD allows with the help of a video or a laser camera and a data glove the automatic generation the necessary robot program for the assembly task. In addition, the demonstration of the task with few different assembly situations and strategies may achieve a generalized assembly function for all possible variants of the class. In order to realize such a PbD system at least two major problems have to be solved. First, the sensor data trace of a demonstration has to be interpreted and transformed into a high-level situation-action representation. This task is not yet well understood nor solved in general. Second, if a generalization is required, induction algorithms must be applied to the sensor data trace, to find the most general user-intended robot function from only few examples. In this paper mainly the second problem is focused. The described experimental PbD environment consists of an industrial robot, a 6D space mouse used as input device, and some sensors. Various data can be recorded during a demonstration for further processing in the PbD system implemented on a workstation. The objective is to exploit the possibilities of integrating learning and clustering algorithms for automated robot programming. In particular it is investigated how human interaction with the PbD system as well as user-initiated dialogs can support inductive learning to acquire generalized assembly programs and skills.