Early Reactive Grasping with Second Order 3D Feature Relations
One of the main challenges in the field of robotics is to make robots ubiquitous. To intelligently interact with the world, such robots need to understand the environment and situations around them and react appropriately, they need context-awareness. But how to equip robots with capabilities of gathering and interpreting the necessary information for novel tasks through interaction with the environment and by providing some minimal knowledge in advance? This has been a longterm question and one of the main drives in the field of cognitive system development.
The main idea behind the work presented in this paper is that the robot should, like a human infant, learn about objects by interacting with them, forming representations of the objects and their categories that are grounded in its embodiment. For this purpose, we study an early learning of object grasping process where the agent, based on a set of innate reflexes and knowledge about its embodiment. We stress out that this is not the work on grasping, it is a system that interacts with the environment based on relations of 3D visual features generated trough a stereo vision system. We show how geometry, appearance and spatial relations between the features can guide early reactive grasping which can later on be used in a more purposive manner when interacting with the environment.
KeywordsUnknown Object Arbitrary Object Stereo Vision System Grasp Planning Shape Primitive
Unable to display preview. Download preview PDF.
- 1.Azad, P., Asfour, T., Dillmann, R.: Combining appearance-based and model-based methods for real-time object recognition and 6d localization. In: IEEE International Conference on Intelligent Robots and Systems (2006)Google Scholar
- 2.Bicchi, A., Kumar, V.: Robotic grasping and contact: A review. In: IEEE International Conference on Robotics and Automation, pp. 348–353 (2000)Google Scholar
- 3.Ding, D., Liu, Y.H., Wang, S.: Computing 3-d optimal formclosure grasps. In: IEEE International Conference on Robotics and Automation, pp. 3573–3578 (2000)Google Scholar
- 4.Fitzpatrick, P., Metta, G., Natale, L., Rao, S., Sandini, G.: Learning About Objects Through Action - Initial Steps Towards Artificial Cognition. In: IEEE International Conference on Robotics and Automation, pp. 3140–3145 (2003)Google Scholar
- 5.Hauck, A., Rüttinger, J., Sorg, M., Färber, G.: Visual Determination of 3D Grasping Points on Unknown Objects with a Binocular Camera System. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 272–278 (1999)Google Scholar
- 9.Krüger, N., Lappe, M., Wörgötter, F.: Biologically motivated multi-modal processing of visual primitives. The Interdisciplinary Journal of Artificial Intelligence and the Simulation of Behaviour 1(5), 417–428 (2004)Google Scholar
- 11.Miller, A.T., Allen, P.: Graspit!: A versatile simulator for grasping analysis. In: ASME International Mechanical Engineering Congress and Exposition (2000)Google Scholar
- 12.Miller, A.T., Knoop, S., Allen, P.K., Christensen, H.I.: Automatic grasp planning using shape primitives. In: IEEE International Conference on Robotics and Automation, pp. 1824–1829 (2003)Google Scholar
- 14.Morales, A., Recatalá, G., Sanz, P.J., del Pobil, Á.P.: Heuristic Vision-Based Computation of Planar Antipodal Grasps on Unknown Objects. In: IEEE International Conference on Robotics and Automation, pp. 583– 588 (2001)Google Scholar
- 15.Platt Jr., R., Fagg, A.H., Grupen, R.A.: Extending fingertip grasping to whole body grasping. In: International Conference on Robotics and Automation, pp. 2677–2682 (2003)Google Scholar
- 16.Pollard, N.S.: Parallel methods for synthesizing whole-hand grasps from generalized prototypes. PhD thesis, Dept. of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (1994)Google Scholar
- 18.Pugeault, N., Wörgötter, F., Krüger, N.: Multi-modal scene reconstruction using perceptual grouping constraints. In: Proceedings of the 5th IEEE Computer Society Workshop on Perceptual Organization in Computer Vision (in conjunction with IEEE CVPR 2006) (2006)Google Scholar
- 19.Rössler, B., Zhang, J., Knoll, A.: Visual Guided Grasping of Aggregates using Self-Valuing Learning. In: IEEE International Conference on Robotics and Automation, pp. 3912–3917 (2002)Google Scholar
- 20.Rutishauser, M., Stricker, M.: Searching for Grasping Opportunities on Unmodeled 3D Objects. In: British Machine Vision Conference, pp. 277–286 (1995)Google Scholar
- 21.Stoytchev, A.: Behavior-Grounded Representation of Tool Affordances. In: IEEE International Conference on Robotics and Automation, pp. 3060–3065 (2005)Google Scholar