Folding Assembly by Means of Dual-Arm Robotic Manipulation

In this paper, we consider folding assembly as an assembly primitive suitable for dual-arm robotic assembly, that can be integrated in a higher level assembly strategy. The system composed by two pieces in contact is modelled as an articulated object, connected by a prismatic-revolute joint. Different grasping scenarios were considered in order to model the system, and a simple controller based on feedback linearisation is proposed, using force torque measurements to compute the contact point kinematics. The folding assembly controller has been experimentally tested with two sample parts, in order to showcase folding assembly as a viable assembly primitive.Comment: 7 pages, accepted for ICRA 201

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

Human-Robot Control Strategies for the NASA/DARPA Robonaut

The Robotic Systems Technology Branch at the NASA Johnson Space Center (JSC) is currently developing robot systems to reduce the Extra-Vehicular Activity (EVA) and planetary exploration burden on astronauts. One such system, Robonaut, is capable of interfacing with external Space Station systems that currently have only human interfaces. Robonaut is human scale, anthropomorphic, and designed to approach the dexterity of a space-suited astronaut. Robonaut can perform numerous human rated tasks, including actuating tether hooks, manipulating flexible materials, soldering wires, grasping handrails to move along space station mockups, and mating connectors. More recently, developments in autonomous control and perception for Robonaut have enabled dexterous, real-time man-machine interaction. Robonaut is now capable of acting as a practical autonomous assistant to the human, providing and accepting tools by reacting to body language. A versatile, vision-based algorithm for matching range silhouettes is used for monitoring human activity as well as estimating tool pose

Reliable non-prehensile door opening through the combination of vision, tactile and force feedback

Whereas vision and force feedback—either at the wrist or at the joint level—for robotic manipulation purposes has received considerable attention in the literature, the benefits that tactile sensors can provide when combined with vision and force have been rarely explored. In fact, there are some situations in which vision and force feedback cannot guarantee robust manipulation. Vision is frequently subject to calibration errors, occlusions and outliers, whereas force feedback can only provide useful information on those directions that are constrained by the environment. In tasks where the visual feedback contains errors, and the contact configuration does not constrain all the Cartesian degrees of freedom, vision and force sensors are not sufficient to guarantee a successful execution. Many of the tasks performed in our daily life that do not require a firm grasp belong to this category. Therefore, it is important to develop strategies for robustly dealing with these situations. In this article, a new framework for combining tactile information with vision and force feedback is proposed and validated with the task of opening a sliding door. Results show how the vision-tactile-force approach outperforms vision-force and force-alone, in the sense that it allows to correct the vision errors at the same time that a suitable contact configuration is guaranteed.This research was partly supported by the Korea Science and Engineering Foundation under the WCU (World Class University) program funded by the Ministry of Education, Science and Technology, S. Korea (Grant No. R31-2008-000-10062-0), by the European Commission’s Seventh Framework Programme FP7/2007-2013 under grant agreements 217077 (EYESHOTS project), and 248497(TRIDENT Project), by Ministerio de Ciencia e Innovación (DPI-2008-06636; and DPI2008-06548-C03-01), by Fundació Caixa Castelló-Bancaixa (P1-1B2008-51; and P1-1B2009-50) and by Universitat Jaume I

Autonomous vision-guided bi-manual grasping and manipulation

This paper describes the implementation, demonstration and evaluation of a variety of autonomous, vision-guided manipulation capabilities, using a dual-arm Baxter robot. Initially, symmetric coordinated bi-manual manipulation based on kinematic tracking algorithm was implemented on the robot to enable a master-slave manipulation system. We demonstrate the efficacy of this approach with a human-robot collaboration experiment, where a human operator moves the master arm along arbitrary trajectories and the slave arm automatically follows the master arm while maintaining a constant relative pose between the two end-effectors. Next, this concept was extended to perform dual-arm manipulation without human intervention. To this extent, an image-based visual servoing scheme has been developed to control the motion of arms for positioning them at a desired grasp locations. Next we combine this with a dynamic position controller to move the grasped object using both arms in a prescribed trajectory. The presented approach has been validated by performing numerous symmetric and asymmetric bi-manual manipulations at different conditions. Our experiments demonstrated 80% success rate in performing the symmetric dual-arm manipulation tasks; and 73% success rate in performing asymmetric dualarm manipulation tasks

A survey of robot manipulation in contact

In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of (1) performing tasks that always require contact and (2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is a more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks