Programming of service robots is an expensive and difficult task especially when manipulator arms are involved. This is one of the drawbacks for every day use of these systems. Programming by Demonstration is a means to let users program robots simply by demonstrating a task like putting a table or composing an object to a system that observes, interprets and then maps the performed user action to a given manipulator. At our institute, observation of a task is realised using active vision systems, a data glove and magnetic field sensors. After the demonstration, the system interprets and stores the recorded actions segmenting them into meaningful parts like grasps of objects or sticking objects to others. Due to sensor errors and the complexity of the problems, the system puts queries concerning grasp types or object positions. A robot should then be able to perform the same actions in a likewise environment. So far, execution has shown good results in simulation. An appropriate service robot is currently being set up at our institute in order to prove the feasibility of our approach. It is equipped with a stereo camera head and a seven DOF manipulator arm with a three finger gripper.

: We present an architecture to generate behavior for an anthropomorphic robot. The goal is to equip the robot with the capacity to interact with a human. Motivated by the research on biological systems, our basic assumption is that the behavior to perform determines the external and internal structure of the behaving system. We describe the anthropomorphic design of our robot and present a distributed control system that generates human-like navigation and manipulation behavior. As the mathematical framework for this purpose we have developed a control system which is entirely based on dynamical systems in the form of instantiated dynamics and neural fields. We also present a dynamic scheme for behavioral organization based on competitive dynamics. 1 Introduction The behavior of living organisms is based on their own sensory information. As the acquisition of sensor information is an active process which changes the state of the system within the environment, the behaving system dir...

This paper gives an overview of the bottom-up approach to artificial intelligence (AI), commonly referred to as behavior-oriented AI. The behavior-oriented approach, with its focus on the interaction between autonomous agents and their environments, is introduced by contrasting it with the traditional approach of knowledge-based AI. Different notions of autonomy are discussed, and key problems of generating adaptive and complex behavior are identified. A number of techniques for the generation of behavior are introduced and evaluated regarding their potential for realizing different aspects of autonomy as well as adaptivity and complexity of behavior. It is concluded that in order to realize truly autonomous and intelligent agents, the behavior-oriented approach will have to focus even more on life-like qualities in both agents and environments.

Dual Dynamics (DD) is a mathematical model of a behavior control system for mobile autonomous robots. Behaviors are specified through di#erential equations, forming a global dynamical system made of behavior subsystems which interact in a number of ways. DD models can be directly compiled into executable code. The article (i) explains the model, (ii) sketches the Dual Dynamics Designer (DDD) environment that we use for the design, simulation, implementation and documentation, and (iii) illustrates our approach with the example of kicking a moving ball into a goal. 1 Introduction In the RoboCup mid-size league, robots have to kick a ball into the right direction. For many reasons, this is a hard task, which calls for robotic methods from many fields: 1. The situation on the field changes rapidly and drastically. This suggests a reactive, behavior-based approach to robot control [ Brooks, 1991 ] . 2. Kicking a moving ball is a continuous and dynamic task. Methods from con...

This paper describes a generalized mathematical approach to generate and organize robot behavior. For the example of a simulated robot which can perform a number of elementary behaviors we demonstrate how the internal behavioral state of the system can be influenced by external inputs. The inputs consist of keyword command strings given by an operator and of depth information determined by the simulation environment. The robot's behavioral state is defined by the activity pattern of the set of elementary behaviors and by the values of the continuous variables that control these elementary behaviors. Sensor information acts on the system by specifying stable states for the dynamics. Behavioral stability is guaranteed by a number of mathematically well defined design principles which must be obeyed when designing behavior generating systems following our approach. In this paper we describe the characteristics of this approach before we present the simulation of a robo...

This paper is complementary to a previous review of signicant research on adaptive behavior in animats. It summarizes the current stateof -the art and outlines directions for possible progress. 1. Introduction In the proceedings of SAB94, we published a review of signicant research on adaptive behavior in animats since the rst SAB conference, held in 1990 (MEYE94). This review summarized the state-of-the art, insofar as the proceedings of three dedicated conferences could help delineate it. Now that three other SAB conferences have been held, we considered that it would be useful to update that earlier review, in order to assess the corresponding progress, to infer the directions in which interesting developments are likely to be expected, and to stress needs for specic additional research eorts. As in the preceding review, this one makes reference only to SAB conference proceedings (SAB96, SAB98, SAB00), on the premise that this perspective, although voluntarily limited, d...

Introduction Autonomous mobile robots for human environments must cope with the main problems that are discussed in autonomous agents research: They operate in dynamic, imperfectly sensed environments, the effect of actions is not precisely predictable and the world is dynamic itself, especially if the robot operates close to humans. Several approaches have been proposed to control robots in such environments, many of them belong to the group of behavior-based robot architectures [5][10][8]. These approaches have in common, that sensing and acting is done closed loop and thus the robot operates mainly reactive on the bottom layer of these architectures. In 1995 Schoner, Dose and Engels introduced the Dynamic Approach to autonomous robot architectures [11]. They presented the idea of robot control by means of nonlinear differential equations in an abstract state space. Such dynamical systems are used to realize paths of sub-symbolic data flow fro

The ultimate task for today's autonomous robots is the probleln of man-machine interaction. This task sets high standards with respect to flexibility, stability, safety and efficiency of the generated robot behavior. We present a control architecture which is capable of both: the generation and organization of stable elementary behaviors based on low-level sensor infbrlnation and the ilnplementation of cognitive concepts like lnelnory, internal simulation and learning. Based on the so-called Dynamic Approach to Robotics our approach tries to mimic the basic working principles of nervous systems of biological organisms. Pushing the parallels to biology even further we restrict ourselves to so-called anthrvpomorphic robots xvhich have silnilarities with the human conceruing the external body form and the sensor equiprecur. Copyright Contvlo 2000 Keywords: Dynanfical Systems, Anthropomorphy, Robotics, Mm-Machine Imeraction, Cognition 1.