This paper shows the application of a systematic approach for constraint-based task specification for sensorbased robot systems [1] to a laser tracing example. This approach integrates both task specification and estimation of geometric uncertainty in a unified framework. The framework consists of an application independent control and estimation scheme. An automatic derivation of controller and estimator equations is achieved, based on a geometric task model that is obtained using a systematic task modeling procedure. The paper details the systematic modeling procedure for the laser tracing task and elaborates on the task specific choice of two types of task coordinates: feature coordinates, defined with respect to object and feature frames, which facilitate the task specification, and uncertainty coordinates to model geometric uncertainty. Furthermore, the control and estimation scheme for this specific task is studied. Simulation and real world experimental results are presented for the laser tracing example.

This paper presents a contribution to programming by human demonstration, in the context of compliant-motion task specification for sensor-controlled robot systems that physically interact with the environment. One wants to learn about the geometric parameters of the task and segment the total motion executed by the human into subtasks for the robot, that can each be executed with simple compliant-motion task specifications. The motion of the human demonstration tool is sensed with a 3-D camera, and the interaction with the environment is sensed with a force sensor in the human demonstration tool. Both measurements are uncertain, and do not give direct information about the geometric parameters of the contacting surfaces, or about the contact formations (CFs) encountered during the human demonstration. The paper uses a Bayesian sequential Monte Carlo method (also known as a particle filter) to do the simultaneous estimation of the CF (discrete information) and the geometric parameters (continuous information). The simultaneous CF segmentation and the geometric parameter estimation are helped by the availability of a contact state graph of all possible CFs. The presented approach applies to all compliant-motion tasks involving polyhedral objects with a known geometry, where the uncertain geometric parameters are the poses of the objects. This work improves the state of the art by scaling the contact estimation to all possible contacts, by presenting a prediction step based on the topological information of a contact state graph, and by presenting efficient algorithms that allow the estimation to operate in real time. In real-world experiments, it is shown that the approach is able to discriminate in real time between some 250 different CFs in the graph

This paper describes the construction of a local polyhedral 3-D feature model derived from pose and wrench sensor measurements collected during a force-controlled execution of a sequence of polyhedral contact formations. The procedure consists of two steps: 1) the sensor measurements are filtered, resulting in a (nonminimal) representation of a 3-D feature model; and 2) identification of a reduced set of geometric parameters (vertices and faces) of the rigid polyhedral objects in the environment is performed. The following improvements with respect to the state of the art are made: 1) creation of a polyhedral 3-D feature model of a previously unknown polyhedral environment; 2) estimation of a force decomposition useful for feedback in a force controller or for monitoring the contact forces; and 3) reduction of the number of model parameters, leading to a computational reduction, a better precision of the geometric parameters, and a higher level description of the contact topology

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.