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.

This work presents new experimental results for the estimation of large position and orientation inaccuracies of contacting objects during force-controlled compliant motion. The estimation is based on position, velocity, and force measurements. The authors have described the contact modeling and presented some Kalman filter identification results for small inaccuracies. However, when dealing with larger inaccuracies, the nonlinear estimation problem remained unsolved. This problem has now been solved satisfactorily by applying a new Bayesian estimator. The Bayesian filter is valid for static systems (parameter estimation) with any kind of nonlinear measurement equation, subject to Gaussian measurement uncertainty and for a limited class of dynamic systems. Experimental results of this new filter are given for the estimation of the positions and orientations of contacting objects during the cube-in-corner assembly described in the first reference.

Autonomous execution of robot tasks requires the ability to deal online with uncertainties such as partially unknown environments, inaccurate models, and measurement noise. This is especially true for the execution of motions maintaining stiff contacts ("compliant motions"), as contact forces become very high even for small position errors. The autonomy during compliant motion tasks is based on i) a force controller, dealing with small misalignments and keeping the contact forces within safe limits, and ii) an estimator, which recognizes the model (e.g., the type of contact) and estimates the system state (e.g., the relative position of the contacting objects). This paper focuses on Bayesian model-based solutions to the model recognition problem. We discuss Bayesian hypothesis testing and practical approximations. Experimental results are provided for two autonomous-compliant motion tasks by applying consistency testing and likelihood ratio testing. The system state is estimated simultaneously with the model recognition. This estimation is performed by the Iterated Extended Kalman filter for (approximate) linear problems and by the nonminimal state Kalman filter for nonlinear problems.

This paper presents a new filter developed for exact nonlinear Bayesian state estimation and model recognition. The filter is able to deal (i) with static systems with any kind of nonlinear measurement function subject to additive Gaussian measurement uncertainty, and (ii) with a limited class of dynamic systems. Experimental position estimation and shape recognition results during a contouring task show the strength of the new nonlinear filtering approach.

This paper describes the optimization of a compliant motion task plan which guarantees the estimation of the inaccurately known position and orientation of the contacting objects to the required accuracy ("active sensing"). The task plan consists of: (1) the desired sequence of contact formations; and (2) the compliant path to execute during each of these contact formations. The plan is optimized to minimize the expected execution time, an important criterion in industrial applications. The theory is implemented for contacts between polyhedral objects.

This paper describes the contact formation modeling for the identification of geometrical parameters (positions, orientations, and dimensions) of rigid polyhedral objects during the force-controlled execution of contact formation sequences. The following improvements with respect to the state of the art are made: 1) the modeling effort is reduced considerably; 2) the generation of the measurement equations for the online estimators can be automated more easily; 3) the propagation of the geometrical parameter estimates over sequences of contact formations becomes straightforward; and 4) the measurement equations are valid for large uncertainties on the geometrical parameter estimates.

This paper gives an overview of the different levels of sensor processing complexity found in force controlled tasks, and explains which techniques from Bayesian probability theory are appropriate to cope with uncertainties and missing information at each of the different levels. The paper reduces all approaches for "intelligent" compliant, motion sensor processing to a basis set of just four classes. Some of these algorithms have already been tested experimentally, while others are still beyond the current state-of-the-art. The major contribution of this paper is to bring a clear structure to the field, which should eventually result in an easier integration of different research results, and a more precise discussion about their relative merits and innovations.