This year's conference focuses on human-robot interaction in the real world. The panel discussion presents the view from those who are "living it": industry leaders who are relying on current robotic technology to accomplish their work right now. Who better to provide the most compelling information on issues/challenges that are influencing their use? This panel gathers experts from business and industry to discuss their experiences in using robotic technology in their field. Topics include the financial, organizational, and practical challenges faced by professionals using robotic technology in the workplace, and factors influencing the acceptance of robots at work. The implications for design of robotic products and systems are also discussed. The session is a must for professionals and academic researchers interested in solving problems related to using robots in real world settings.

The dynamics and control of a cable-suspended, two-arm robotic system are developed for an entertainment application. One manipulator arm is controlled to fulfil a user defined task. The second arm is then controlled to compensate for the disturbances on the cable-suspended platform arising from the motion of the first. Model-based feedforward control, stemming from the momentum conservation equations of a free-floating robot, is developed for the motion compensation problem. Furthermore, due to model uncertainty, sensor-based feedback control is introduced, to account for undesired oscillatory motions of the system. The latter control problem reduces to the dissipation of the oscillatory energy of the system, by means of adequate robot control. Both control methods are implemented and tested on an experimental set-up

This work describes a data fusion architecture for robotic assembly tasks based on human sensory-motor skills. These skills are transferred to the robot through geometric and dynamic perception signals. Artificial neural networks are used in the learning process. The data fusion paradigm is addressed. It consists of two independent modules for optimal fusion and filtering. Kalman techniques linked to stochastic signal evolutions are used in the fusion algorithm. Compliant motion signals obtained from vision and pose sense are fused, enhancing the task performance. Simulations and peg-in-hole experiments are reported.

The paper discusses new developments of the data fusion paradigm due to Cortesao and Koeppe (1999, 2000). A bank of Kalman filters is analyzed in the fusion process. Experiments for a robotic compliant motion task (peg-in-hole) emerged from human skills are reported. Stereo vision and pose sense are fused to execute the task. Feedforward artificial neural networks (ANNs) are trained to transfer human skills to robotic manipulators.

The theory of active observers was initially described by Cortesao et al. (2000). This paper introduces properties of the Kalman gains used in the active observer (AOB) design. An important result of the state-space design is demonstrated, which allows stiffness adaptation without changing the control structure. Experiments with a human-robot skill transfer system to perform the peg-in-hole compliant motion task are described, showing the importance of the AOB.

The article describes a systematic procedure to design a force controller with active observers (AOB). The design is based on pole placement using discrete state space theory. The concept of AOB is introduced as a starting point to perform robust estimates of the system state. The robustness of the system is accomplished through optimal noise processing embedded in the control strategy. The design was tested as a force controller in a Manutec R2 Robot at the DLR.

Architectures for the execution of autonomous compliant motion (ACM) require modules for 6 degree-of-freedom localization, navigation and force control of robot manipulators. We review existing model- and signal-based approaches to ACM and present a solution for the synthesis of localization and navigation using sensorimotor signals recorded from human demonstration.

In our previous work, several system elements for space teleoperation were introduced: a Singularity Consistent (SC) teleoperation system, a mixed force and motion commands based space teleoperation system, and a 6-DOF haptic interface (Y. Tsumaki and M. Uchiyama, 1997; Y. Tsumaki et al., 1998). We introduce an advanced space teleoperation system which includes all the above elements. As a result, the system realizes safety against the singularities and excessive force, with good perception for the operator. In addition, to verify the effectiveness of this system, we conducted experiments through the Internet between DLR in Germany and Tohoku University in Japan in January 1999.

Describes how to design a data fusion module for skill transfer purposes. The data fusion paradigm is addressed. It consists of two independent modules for optimal fusion and filtering. A new interpretation of the Kalman filter equations is done, to achieve a "model-free" equation capable of following arbitrary variables. An engineering approach is used to tune the parameters of interest for a certain task. The fusion algorithm presented is global, and could easily be extended to any arbitrary system. It was successfully tested in the Institute of Robotics and System Dynamics at the DLR.

Most of the approaches proposed in the field of sensor based assembly use force and velocity, i.e., sensor signals representing dynamics, as a base on which a desired compliant motion strategy is specified. In contrast, especially while performing alignment and insertion tasks the problem of sensing the assembly state of two objects w.r.t. each other is of geometric nature. Using a generic sensorimotor skill model for compliant motion tasks we discuss the difference between dynamic and geometric perception. We propose approaches of kinesthetic and visual sensing and show the robustness of compliant motion skill maps obtained from sensor signals representing the task geometry rather than the task dynamics.

Compliant motion tasks can be programmed by human demonstration without the use of the actual robot system. In space applications and sensor off-line programming systems the task is planned and programmed in a virtual reality environment which simulates the world model of the task to be performed. Furthermore, in industrial and service applications teaching devices measuring the force and motion of the human hand allow the operator to demonstrate a task in a very natural way. By using dynamic manipulability analysis of motion and force, we investigate if a compliant motion trajectory obtained by the above described demonstration processes can be executed on a given robot. We analyze the maximum task workspace of executability, the optimal robot-task configuration and the range of necessary or possible scaling factors for motion and forces. The approach is demonstrated using a numerical example.

We are proposing a supervised learning approach in robot force control which enables robot programming by human demonstration. To demonstrate the task in an intuitive way, we designed a teach device which acquires the sensorimotion of the human. The design and use of the teaching device for skill acquisition is discussed. Recording of compliant motion, of the human is shown for the "put block in a corner of a box problem" and the "peg in hole problem". Skill representation is demonstrated for the first of the two problems. Finally we are describing other useful applications of the teach device in the field of world modeling and robotics.