This paper presents a unified task specification formalism and a unified control scheme for the lowest control level of sensor-based robot tasks. The formalism is based on: (i) the integration of any sensor that provides (direct or indirect) distance (and time derivatives) and force information; (ii) the possibility to use multiple "Tool Centre Points", e.g. defined relative to the robot end effector, other links or the environment; (iii) the integration of optimization functions for underconstrained as well as overconstrained specifications with linear constraints; (iv) the integration of on-line estimators; and (v) compatibility with all major low level control approaches. The unified formalism applies to the whole range from industrial manipulators over cooperating robots to humanoid robots, and from pure position control tasks over industrial processes to interaction between a humanoid robot and its environment.

This paper presents a modular demonstration tool for robot programming by human demonstration and an approach for the calibration of the tool's sensors. The tool is equipped with a wrench sensor, twelve LED markers for fast and accurate six dimensional position tracking with the Krypton K600 camera system, a compact camera and a laser distance sensor. A gripper mechanism is mounted on the tool for grasping and manipulating objects. The design of the tool specifically focused on the demonstration of compliant motion task, with applications in manipulation and assembly tasks. The calibration approach first uses an extended Kalman Filter to convert the measured positions of three to twelve visible LED's into the pose of the tool frame relative to the Krypton camera frame. Then, using a non minimal state Kalman filter, the force sensor calibration parameters are calculated, and the orientation of the Krypton camera frame relative to the world frame is defined. This calibration approach is verified in a real world experiment.

This paper presents a method to improve the path tracking accuracy of an industrial robot without replacing the standard industrial controller. By calculating off-line an appropriate trajectory pre-compensation, the effects of the nonlinear dynamics are compensated. This is realized by filtering the desired trajectory with the inverse dynamic model of the robot and its velocity controller. This compensation is applied as a velocity feedforward in the standard industrial controller avoiding the need for a torque control interface. The method presented is validated experimentally on a KUKA IR 361 industrial robot. The results show clearly an improved path tracking accuracy on circular trajectories.

Experimental robot identification techniques can principally be divided into two categories, based on the type of models they use: internal or external. Internal models relate the joint torques or forces and the motion of the robot; external models relate the reaction forces and torques on the bedplate and the motion data. This paper describes how internal and external robot models can be combined into one identifiable minimal model. This model allows to combine joint torque/force and reaction torque/force measurements in one parameter estimation scheme. This combined model estimation will yield more accurate parameter estimates, and consequently better actuator torque predictions, which is shown experimentally on an industrial robot (KUKA IR 361).