Robot Active Touch Exploration: Constraints and Strategies

We investigate the problem of using active touch ("haptic") exploration to recognize a 3D object taken from a known set of models. "That is new is that we combine two approaches: (1) using geometric constraints between components to eliminate interpretations, and interpretation tree methods for choosing the best active sensing move; (2) exploratory moves made by tracing continually along the surface of the object (and not through free space). We restrict ourselves to polyhedral, and give a set of geometric constraints tailored for matching components acquired from haptic exploration against components in the models. We present a new constraint using pairs of line segments. We then give a set of active sensing moves, each with an associated cost measure, and our strategies for choosing the next move

Robust grasping under object pose uncertainty

This paper presents a decision-theoretic approach to problems that require accurate placement of a robot relative to an object of known shape, such as grasping for assembly or tool use. The decision process is applied to a robot hand with tactile sensors, to localize the object on a table and ultimately achieve a target placement by selecting among a parameterized set of grasping and information-gathering trajectories. The process is demonstrated in simulation and on a real robot. This work has been previously presented in Hsiao et al. (Workshop on Algorithmic Foundations of Robotics (WAFR), 2008; Robotics Science and Systems (RSS), 2010) and Hsiao (Relatively robust grasping, Ph.D. thesis, Massachusetts Institute of Technology, 2009).National Science Foundation (U.S.) (Grant 0712012

Grasp planning under uncertainty

Advanced robots such as mobile manipulators offer nowadays great opportunities for realistic manipulators. Physical interaction with the environment is an essential capability for service robots when acting in unstructured environments such as homes. Thus, manipulation and grasping under uncertainty has become a critical research area within robotics research. This thesis explores techniques for a robot to plan grasps in presence of uncertainty in knowledge about objects such as their pose and shape. First, the question how much information about the graspable object the robot can perceive from a single tactile exploration attempt is considered. Next, a tactile-based probabilistic approach for grasping which aims to maximize the probability of a successful grasp is presented. The approach is further extended to include information gathering actions based on maximal entropy reduction. The combined framework unifies ideas behind planning for maximally stable grasps, the possibilities of sensor-based grasping and exploration. Another line of research is focused on grasping familiar object belonging to a specific category. Moreover, the task is also included in the planning process as in many applications the resulting grasp should be not only stable but task compatible. The vision-based framework takes the idea of maximizing grasp stability in the novel context to cover shape uncertainty. Finally, the RGB-D vision-based probabilistic approach is extended to include tactile sensor feedback in the control loop to incrementally improve estimates about object shape and pose and then generate more stable task compatible grasps. The results of the studies demonstrate the benefits of applying probabilistic models and using different sensor measurements in grasp planning and prove that this is a promising direction of study and research. Development of such approaches, first of all, contributes to the rapidly developing area of household applications and service robotics

Object Recognition Using Vision and Touch

A robotic system for object recognition is described that uses both active exploratory tactile sensing and passive stereo vision. The complementary nature of these sensing modalities allows the system to discover the underlying three dimensional structure of the objects to be recognized. This structure is embodied in rich, hierarchical, viewpoint independent 3-D models of the objects which include curved surfaces, concavities and holes. The vision processing provides sparse 3-D data about regions of interest that are then actively explored by the tactile sensor which is mounted on the end of a six degree of freedom manipulator. A robust hierarchical procedure has been developed to integrate the visual and tactile data into accurate three dimensional surface and feature primitives. This integration of vision and touch provides geometric measures of the surfaces and features that are used in a matching phase to find model objects that are consistent with the sensory data. Methods for verification of the hypothesis are presented, including the sensing of visually occluded areas with the tactile sensor. A number of experiments have been performed using real sensors and real, noisy data to demonstrate the utility of these methods and the ability of such a system to recognize objects that would be difficult for a system using vision alone

Development of a Method for the Description of Gripping Processes

The variety of tasks that can be performed by a robot and the enormous range of components, different for shape, size, material and weight that can be manipulated, make the use of only one typology of gripper for an entire handling process more difficult. Beyond this, today, the choice of the gripper that best fits a specific task is still made intuitively and based on the worker’s experience. This leads to the generation of extra costs due to the implementation of not working solutions and to wasting a lot of time in searching for feasible configurations. A new approach able to help in the systematical analysis of the different types of handling processes is therefore required. Unfortunately, the existing techniques and methodologies for the selection of a gripper do not provide an approach suitable for all the handling tasks, because, in most cases, the work is limited to the analysis of just one category of gripper and of the only few parameters that visibly influenced its performances. Even the approaches that have been found to be more complete, do not investigate the possible links existing among the parameters through which the process can be described and therefore, they lose the capability to forecast the consequences that a change of working situation can have on the gripper choice and hence on the task outcome. In order to face these deficiencies, the goal of the present work is to develop a method to describe gripping processes through the representation of the factors that should be set up in order to achieve an optimal task implementation and of the relationships existing between them. After reviewing the literature regarding parameters that characterize a handling task, this thesis develops a sensitivity analysis in order to delineate the trend that a change in a parameter of the task can produce on the other process variables. The implementation of the method is then provided on MATLAB with the construction of a Graphical User Interface that allows the calculation of the quantitative values associated to each parameter. The conclusion of the work is the validation, which is applied to the model in order to confirm the results obtained. The validation regards three cases of real working applications: the first is a pick and place process executed by a vacuum gripper, the second and the third cases, instead, deal with an assembly and disassembly processes respectively and are performed by a parallel gripper

Haptische Objekterkennung mit einer humanoiden Roboterhand

Der Fokus dieser Arbeit liegt auf der Analyse haptischer Sensordaten einer humanoiden Roboterhand mit dem Ziel der Erkennung der Objektform. Der Leitfaden ist dabei die Betrachtung unterschiedlicher Fusionsansätze für haptische Sensordaten, mit denen sich Objekte sowohl grob in wenigen als auch detailliert mit mehreren Abtastungen unterscheiden lassen. Ein Teil des vorgestellten Systems ist ein Aufmerksamkeitsraum, der eine Abtaststrategie und damit eine aktive Klassifikation ermöglicht