TechTalks from event: Technical session talks from ICRA 2012

Conference registration code to access these videos can be accessed by visiting this link: PaperPlaza. Step-by-step to access these videos are here: step-by-step process .
Why some of the videos are missing? If you had provided your consent form for your video to be published and still it is missing, please contact support@techtalks.tv

Parts Handling and Manipulation

  • Design of Parts Handling and Gear Assembling Device Authors: Yamaguchi, Kengo; Hirata, Yasuhisa; Kaisumi, Aya; Kosuge, Kazuhiro
    Many one-degree-of-freedom (1-DOF) grippers have been used in factories. This paper focuses on the design of the 1-DOF parts handling device for picking up small objects robustly and agilely and realizing assembly tasks. In our conventional research, we proposed a concept for the handling device, which cages an object without letting the object escape from its tips before closing them completely and then grasps the object robustly at a unique position of the tips. In this paper, we propose a method for designing the shape of the device's tips by considering not only the caging and self-alignment of the object but also the gear assembly task. We also develop the robust and agile pick-up device (RAPiD) with tips designed by the new method and present experimental results that illustrate the ability of RAPiD to handle and assemble gears.
  • Optimal Admittance Characteristics for Planar Force-Assembly of Convex Polygonal Parts Authors: Wiemer, Steven; Schimmels, Joseph
    Robots are not typically used for assembly tasks in which positioning requirements exceed robot capabilities. To address this limitation, a significant amount of work has been directed toward identifying desirable mechanical behavior of a robot for force-guided assembly. Most of this work has been directed toward the `standard' peg-in-hole assembly problem. Little has been done to identify the specific behavior necessary for reliable assembly for different types of polygonal parts, and little has been done relating assembly characteristics to classes of part geometries. This paper presents the best passive admittance and associated maximum coefficient of friction for planar force-assembly of a variety of different polygonal parts, specifically pegs with rectangular, trapezoidal, triangular, and pentagonal cross sections. The results show that force-guided assembly can be reliably achieved at higher values of friction when parts are shorter and wider. For all geometries considered, force-guided assembly is ensured for any value of friction less than 0.8 when the optimal admittance is used; and, for some geometries, for any value of friction less than 15.
  • The Effect of Anisotropic Friction on Vibratory Velocity Fields Authors: Umbanhowar, Paul; Vose, Thomas; Mitani, Atsushi; Hirai, Shinichi; Lynch, Kevin
    This paper explores the role of anisotropic friction properties in vibratory parts manipulation. We show that direction-dependent surface friction properties can be used in conjunction with a vibrating plate to help design friction-induced velocity fields on the surface of the plate. Theoretical, simulation, and experimental results are presented quantifying the anisotropic friction effects of textured surfaces such as micromachined silicon and fabrics.
  • Sparse Spatial Coding: A Novel Approach for Efficient and Accurate Object Recognition Authors: Leivas, Gabriel; Nascimento, Erickson; Wilson Vieira, Antonio; Campos, Mario Montenegro
    Successful state-of-the-art object recognition techniques from images have been based on powerful methods, such as sparse representation, in order to replace the also popular vector quantization (VQ) approach. Recently, sparse coding, which is characterized by representing a signal in a sparse space, has raised the bar on several object recognition benchmarks. However, one serious drawback of sparse space based methods is that similar local features can be quantized into different visual words. We present in this paper a new method, called Sparse Spatial Coding (SSC), which combines a sparse coding dictionary learning, a spatial constraint coding stage and an online classification method to improve object recognition. An efficient new off-line classification algorithm is also presented. We overcome the problem of techniques which make use of sparse representation alone by generating the final representation with SSC and max pooling, presented for an online learning classifier. Experimental results obtained on the Caltech 101, Caltech 256, Corel 5000 and Corel 10000 databases, show that, to the best of our knowledge, our approach supersedes in accuracy the best published results to date on the same databases. As an extension, we also show high performance results on the MIT-67 indoor scene recognition dataset.
  • Humanoid's Dual Arm Object Manipulation Based on Virtual Dynamics Model Authors: Shin, Sung Yul; Lee, Jun won; Kim, ChangHwan
    In order to implement promising robot applications in our daily lives, robots need to perform manipulation tasks within the human environments. Especially for a humanoid robot, it is essential to manipulate a variety of objects with different shapes and sizes to assist humans in the human environments. This paper presents a method of manipulating objects with humanoid robot's dual arms. The robot is usually asked to control both the motion and force to manipulate the objects. We propose a novel concept of control method based on the virtual dynamics model (VDM), which enables the robot to perform both tasks of reaching to an object and grasping it under the uniform control system. Furthermore, the impedance model based on the VDM controller also enables the robot to safely grasp an object by reducing the impact at the contact point. The proposed algorithm is implemented on the humanoid robot, Mahru, with independent joint controller at each motor. Its performance is demonstrated by manipulating different types of objects.
  • A Kernel-Based Approach to Direct Action Perception Authors: Kroemer, Oliver; Ugur, Emre; Oztop, Erhan; Peters, Jan
    The direct perception of actions allows a robot to predict the afforded actions of observed objects. In this paper, we present a non-parametric approach to representing the affordance-bearing subparts of objects. This representation forms the basis of a kernel function for computing the similarity between different subparts. Using this kernel function, together with motor primitive actions, the robot can learn the required mappings to perform direct action perception. The proposed approach was successfully implemented on a real robot, which could then quickly learn to generalize grasping and pouring actions to novel objects.