A strategy for grasping unknown objects based on co-planarity and colour information

  • Authors:

  • Mila Popović;Dirk Kraft;Leon Bodenhagen;Emre Başeski;Nicolas Pugeault;Danica Kragic;Tamim Asfour;Norbert Krüger

  • Affiliations:

  • Cognitive Vision Lab, The Mærsk McKinney Møller Institute, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark;Cognitive Vision Lab, The Mærsk McKinney Møller Institute, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark;Cognitive Vision Lab, The Mærsk McKinney Møller Institute, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark;Cognitive Vision Lab, The Mærsk McKinney Møller Institute, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark;Centre for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Guildford, UK;Centre for Autonomous Systems, Computational Vision and Active Perception Lab, Royal Institute of Technology, SE-100 44, Stockholm, Sweden;Karlsruhe Institute of Technology, Institute for Anthropomatics, Humanoids and Intelligence Systems Lab, Adenauerring 2, 76131 Karlsruhe, Germany;Cognitive Vision Lab, The Mærsk McKinney Møller Institute, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark

  • Venue:
  • Robotics and Autonomous Systems
  • Year:
  • 2010

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Abstract

In this work, we describe and evaluate a grasping mechanism that does not make use of any specific object prior knowledge. The mechanism makes use of second-order relations between visually extracted multi-modal 3D features provided by an early cognitive vision system. More specifically, the algorithm is based on two relations covering geometric information in terms of a co-planarity constraint as well as appearance based information in terms of co-occurrence of colour properties. We show that our algorithm, although making use of such rather simple constraints, is able to grasp objects with a reasonable success rate in rather complex environments (i.e., cluttered scenes with multiple objects). Moreover, we have embedded the algorithm within a cognitive system that allows for autonomous exploration and learning in different contexts. First, the system is able to perform long action sequences which, although the grasping attempts not being always successful, can recover from mistakes and more importantly, is able to evaluate the success of the grasps autonomously by haptic feedback (i.e., by a force torque sensor at the wrist and proprioceptive information about the distance of the gripper after a gasping attempt). Such labelled data is then used for improving the initially hard-wired algorithm by learning. Moreover, the grasping behaviour has been used in a cognitive system to trigger higher level processes such as object learning and learning of object specific grasping.