This paper describes the construction of a geometric 3-D model from the identification of geometrical parameters (vertices and faces) of rigid polyhedral objects in the environment during the force-controlled execution of contact formation sequences. Following improvements with respect to the state of the art are made: (i) creation of a 3-D model from a previously unknown environment, (ii) the estimation of a force decomposition useful for feedback to a force controller or for monitoring the contact forces, (iii) a method to reduce the number of modelling parameters, leading to a computational reduction, a better precision and a more accurate geometric description.

Discrete event recognition based on force measurements in the frequency-domain is presented. The force signals arise from interaction between the workpiece and the environment in a planar assembly task. The discrete events are modeled as Hidden Markov Models (HMMs), where the models are trained off-line with the Baum-Welch re-estimation algorithm. After the HMMs have been trained, we use them on-line in a robotic system to recognise discrete events as they occur. Event recognition with an accuracy as high as 98% was accomplished in 0.5-0.6s with a relatively small training set. 1 Introduction Process plants must deal with changing states, multiple faults, unexpected situations and unreliable measurements. To handle these problems real-time process monitoring is essential. Process monitoring is widely used as a component in many industrial processes. In applications such as robotic assembly, however, there is an increasing need for efficient process monitoring methods to account for u...

For a given time series observation sequence, we can estimate the parameters of the AutoRegression Moving Average (ARMA) model, thereby representing a potentially long time series by a limited dimensional vector. In many applications, these parameter vectors will be separable into different groups, due to the different underlying mechanisms that generate differing time series. We can then use classification algorithms to predict the class of a new, uncategorized time series. For the purposes of a highly autonomous system, our approach to this classification uses memory -based learning and intensive cross-validation for feature and kernel selection. In an example application, we distinguish between driving data of a skilled, sober driver vs. a drunk driver, by calculating the ARMA model for the respective time series. In this paper, we first give a brief introduction to the theory of time series. We then discuss in detail our approach to time series recognition, using the ARMA model, an...

This paper describes a Bayesian approach to model selection and state estimation for sensor-based robot tasks. The approach is illustrated with an example from autonomous compliant motion: simultaneous contact formation recognition and estimation of 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 interaction between the Contact Formation detection and the geometrical parameter estimation problems. This limits the application area to task execution under small uncertainties. The problem shows similarities with the well known problems of data association in SLAM and model selection in global localisation. The paper discusses an experiment in which the performances of two well known Bayesian algorithms are compared with respect to this problem: Kalman Filter variants and Particle Filter solutions. This research allows the robot to handle large uncertainties during the execution of its sensor-based task through the estimation of a hybrid joint density of both unknown model and state variables.

Objective: Evaluation of the laparoscopic surgical skills of surgical residents is usually a subjective process carried out in the operating room by senior surgeons. The two hypotheses of the current study were: (1) haptic information and tool/tissue interactions (types and transitions) performed in laparoscopic surgery are skill-dependent, and (2) statistical models (Hidden Markov Models---HMMs) incorporating these data are capable of objectively evaluating laparoscopic surgical skills.

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, complimentary 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 modelling 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. 1

Abstract: In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks) are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or Tintersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.

As the capabilities of manipulator robots increase, they are performing more complex tasks. The cumbersome nature of conventional programming methods limits robotic automation due to the lengthy programming time. We present a novel method for reducing the time needed to program a manipulator robot: Predictive Robot Programming (PRP). The PRP system constructs a statistical model of the user by incorporating information from previously completed tasks. Using this model, the PRP system computes predictions about where the user will move the robot. The user can reduce programming time by allowing the PRP system to complete the task automatically. In this paper, we derive a learning algorithm that estimates the structure of continuous-density hidden Markov models from tasks the user has already completed. We analyze the performance of the PRP system on two sets of data. The first set is based on data from complex, real-world robotic tasks. We show that the PRP system is able to compute predictions for about 25% of the waypoints with a median prediction error less than 0:5% of the distance traveled during prediction. We also present laboratory experiments showing that the PRP system results in a significant reduction in programming time, with users completing simple robotprogramming tasks over 30% faster when using the PRP system to compute predictions of future positions.

Abstract — In the future, robotic surgical assistants may assist surgeons by performing specific subtasks such as retraction and suturing to reduce surgeon tedium and reduce the duration of some operations. We propose an apprenticeship learning approach that has potential to allow robotic surgical assistants to autonomously execute specific trajectories with superhuman performance in terms of speed and smoothness. In the first step, we record a set of trajectories using human-guided backdriven motions of the robot. These are then analyzed to extract a smooth reference trajectory, which we execute at gradually increasing speeds using a variant of iterative learning control. We evaluate this approach on two representative tasks using the Berkeley Surgical Robots: a figure eight trajectory and a two handed knot-tie, a tedious suturing sub-task required in many surgical procedures. Results suggest that the approach enables (i) rapid learning of trajectories, (ii) smoother trajectories than the human-guided trajectories, and (iii) trajectories that are 7 to 10 times faster than the best human-guided trajectories. I.

Ground controlled telerobots can be used to reduce astronaut workload while retaining much of the human capabilities of planning, execution, and error recovery for specific tasks. Miniature robots can be used for delicate and time-consuming tasks such as biological experiment servicing without incurring the significant mass and power penalties associated with larger robot systems. However, questions remain regarding the technical and economic effectiveness such mini-telerobotic systems. This paper addresses some of these open issues and the details of two projects which will be able to provide some of the needed answers. The Microtrex project is a joint University of Washington/NASA project which plans on flying a miniature robot as a Space-shuttle experiment to evaluate the effects of microgravity on groundcontrolled manipulation while subject to variable time-delay communications. A related project involving the University of Washington and Boeing Defense and Space will evaluate the effectiveness of using a minirobot to service biological experiments in a space station experiment "glove-box" rack mock-up, again while subject to realistic communications constraints. I.