Constructing Task-Level Assembly Strategies in Robot Programming by Demonstration

Programming by demonstration (PbD) is a technique for programming robots that holds much promise in making robots more accessible to ordinary, non-technical users. However, a well-known difficulty with the method is that a human will often demonstrate the task to be programmed inconsistently or even erroneously, leading to the inclusion of what is essentially noise in the demonstration. A number of techniques exist in the literature for filtering out this type of noise; however, most focus on very low level control command details. In this paper, we propose a new, complementary direction of research. We take a “task-level” view of the demonstration, and note that noise can exist at this level also. We propose a framework, based on a hybrid dynamic system modeling approach, to select the most optimal, task-level execution strategies that were demonstrated. We apply our framework to a real household task of inserting the compressible spindle of a paper towel holder into its supports. We conduct experiments to show that significant improvements in robot performance of the task can be achieved by a PbD regime that includes our method.

Bayesian Hybrid Model-State Estimation Applied to Simultaneous Contact Formation Recognition and Geometrical Parameter Estimation

In this paper we describe a Bayesian approach to model selection and state estimation for sensor-based robot tasks. The approach is illustrated with a hybrid model-state estimation example from force-controlled autonomous compliant motion: simultaneous (discrete) contact formation recognition and estimation of (continuous) geometrical parameters. Previous research in this area mostly tries to solve one of the two subproblems, or treats the contact formation recognition problem separately, avoiding integration between the solutions to the contact formation recognition and the geometrical parameter estimation problems. A more powerful hybrid model, explicitly modeling contact formation transitions, is developed to deal with larger uncertainties. This paper demonstrates that Kalman filter variants have limits: iterated extended Kalman filters can only handle small uncertainties on the geometrical parameters, while the non-minimal state Kalman filter cannot deal with model selection. Particle filters can handle the increased level of model complexity. Explicit measurement equations for the particle filter are derived from the implicit kinematic and energetic constraints. The experiments prove that the particle filter approach successfully estimates the hybrid joint posterior density of the discrete contact formation variable and the 12-dimensional, continuous geometrical parameter vector during the execution of an assembly task. The problem shows similarities with the well-known problems of data association in simultaneous localization and map-building (SLAM) and model selection in global localization.

Constraint-based Task Specification and Estimation for Sensor-Based Robot Systems in the Presence of Geometric Uncertainty

This paper introduces a systematic constraint-based approach to specify complex tasks of general sensor-based robot systems consisting of rigid links and joints. The approach integrates both instantaneous task specification and estimation of geometric uncertainty in a unified framework. Major components are the use of feature coordinates, defined with respect to object and feature frames, which facilitate the task specification, and the introduction of uncertainty coordinates to model geometric uncertainty. While the focus of the paper is on task specification, an existing velocity- based control scheme is reformulated in terms of these feature and uncertainty coordinates. This control scheme compensates for the effect of time varying uncertainty coordinates. Constraint weighting results in an invariant robot behavior in case of conflicting constraints with heterogeneous units. The approach applies to a large variety of robot systems (mobile robots, multiple robot systems, dynamic human-robot interaction, etc.), various sensor systems, and different robot tasks. Ample simulation and experimental results are presented.

Identification of Contact Conditions by Active Force Sensing - Estimated Parameter Uncertainty and Experimental Verification -

Accomplishing assembly tasks using robots requires that contact conditions between grasped objects and environments be identified and controlled during task execution. We propose estimated parameter uncertainty of contact location and direction, discussing four contact types – point, soft-finger, line, and planar contact. We formulate a least-squares function (i.e., performance index) including contact location and direction. The uncertainty of estimates is derived using the rest error of minimized index. The effectiveness of our proposed method is demonstrated through experiments.