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

A Method of 3D Object Reconstruction by Fusing Vision with Touch Using Internal Models with Global and Local Deformations

PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATIO

Robust visual servoing in 3d reaching tasks

This paper describes a novel approach to the problem of reaching an object in space under visual guidance. The approach is characterized by a great robustness to calibration errors, such that virtually no calibration is required. Servoing is based on binocular vision: a continuous measure of the end-effector motion field, derived from real-time computation of the binocular optical flow over the stereo images, is compared with the actual position of the target and the relative error in the end-effector trajectory is continuously corrected. The paper outlines the general framework of the approach, shows how visual measures are obtained and discusses the synthesis of the controller along with its stability analysis. Real-time experiments are presented to show the applicability of the approach in real 3-D applications

Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search

This paper considers the problem of active object recognition using touch only. The focus is on adaptively selecting a sequence of wrist poses that achieves accurate recognition by enclosure grasps. It seeks to minimize the number of touches and maximize recognition confidence. The actions are formulated as wrist poses relative to each other, making the algorithm independent of absolute workspace coordinates. The optimal sequence is approximated by Monte Carlo tree search. We demonstrate results in a physics engine and on a real robot. In the physics engine, most object instances were recognized in at most 16 grasps. On a real robot, our method recognized objects in 2--9 grasps and outperformed a greedy baseline.Comment: Accepted to International Conference on Intelligent Robots and Systems (IROS) 201

Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search

This paper considers the problem of active object recognition using touch only. The focus is on adaptively selecting a sequence of wrist poses that achieves accurate recognition by enclosure grasps. It seeks to minimize the number of touches and maximize recognition confidence. The actions are formulated as wrist poses relative to each other, making the algorithm independent of absolute workspace coordinates. The optimal sequence is approximated by Monte Carlo tree search. We demonstrate results in a physics engine and on a real robot. In the physics engine, most object instances were recognized in at most 16 grasps. On a real robot, our method recognized objects in 2--9 grasps and outperformed a greedy baseline.Comment: Accepted to International Conference on Intelligent Robots and Systems (IROS) 201

Experiences with the JPL telerobot testbed: Issues and insights

The Jet Propulsion Laboratory's (JPL) Telerobot Testbed is an integrated robotic testbed used to develop, implement, and evaluate the performance of advanced concepts in autonomous, tele-autonomous, and tele-operated control of robotic manipulators. Using the Telerobot Testbed, researchers demonstrated several of the capabilities and technological advances in the control and integration of robotic systems which have been under development at JPL for several years. In particular, the Telerobot Testbed was recently employed to perform a near completely automated, end-to-end, satellite grapple and repair sequence. The task of integrating existing as well as new concepts in robot control into the Telerobot Testbed has been a very difficult and timely one. Now that researchers have completed the first major milestone (i.e., the end-to-end demonstration) it is important to reflect back upon experiences and to collect the knowledge that has been gained so that improvements can be made to the existing system. It is also believed that the experiences are of value to the others in the robotics community. Therefore, the primary objective here will be to use the Telerobot Testbed as a case study to identify real problems and technological gaps which exist in the areas of robotics and in particular systems integration. Such problems have surely hindered the development of what could be reasonably called an intelligent robot. In addition to identifying such problems, researchers briefly discuss what approaches have been taken to resolve them or, in several cases, to circumvent them until better approaches can be developed

Sensing and describing 3-D structure

Discovering the three dimensional structure of an object is important for a variety of robot tasks. Single sensor systems such as machine vision systems cannot reliably compute three dimensional structure in unconstrained environments. Active, exploratory tactile sensing can be used to complement passive stereo vision data to derive robust surface and feature descriptions of objects. The control for tactile sensing is provided by the vision system which provides regions of interest that the tactile system can explore. The descriptions of surfaces and features are accurate and can be used in a later matching phase against a model data base of objects to identify the object and its position and orientation in space

An Application of Robotic Optimization: Design for a Tire Changing Robot

The final publication is available at link.springer.com.Robotics experiences tremendous evolutions every year. Once a topic is mainly approached at research centers, read through highly specialized books and viewed distantly on scientific channels, nowadays it is a common and very approachable subject among undergraduate students of many universities. More and more robots are being designed every day, demanding technological implementation and production. This progress does not come without its glitches, however. A common and increasing problem that appears is the insufficient testing, simulation and optimization steps that a robotic construction needs to pass in order to achieve an efficient design. These steps prove to be difficult and sometimes discouraging, resulting in laborious work, due to lack of tools. This paper presents an example of a robotic optimization and testing, using a generic software package, applied on a custom manipulator, a tire-changing robot. Although the manipulator is designed with its own simulation and control package, it may lack optimality or validity. We implemented a different software package, focused on optimization and control of simple generic robots (XXX.RRR types) and apply the package on the tire-changer manipulator. The results provide improvements for the primary controlling software and confirm its correctness.http://link.springer.com/article/10.1023/A%3A100812123054

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