Abstract
The ability to grasp unknown objects still remains an unsolved problem in the robotics community. One of the challenges is to choose an appropriate grasp configuration, i.e., the 6D pose of the hand relative to the object and its finger configuration. In this paper, we introduce an algorithm that is based on the assumption that similarly shaped objects can be grasped in a similar way. It is able to synthesize good grasp poses for unknown objects by finding the best matching object shape templates associated with previously demonstrated grasps. The grasp selection algorithm is able to improve over time by using the information of previous grasp attempts to adapt the ranking of the templates to new situations. We tested our approach on two different platforms, the Willow Garage PR2 and the Barrett WAM robot, which have very different hand kinematics. Furthermore, we compared our algorithm with other grasp planners and demonstrated its superior performance. The results presented in this paper show that the algorithm is able to find good grasp configurations for a large set of unknown objects from a relatively small set of demonstrations, and does improve its performance over time.
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A short summary of the grasping experiments is also shown at http://www.youtube.com/watch?v=C7_xVxu8_RU.
References
Balasubramanian, R., Xu, L., Brook, P. D., Smith, J. R., & Matsuoka, Y. (2012). Physical human interactive guidance: Identifying grasping principles from human-planned grasps. IEEE Transactions on Robotics, 28(4), 899–910.
Bohg, J., & Kragic, D. (2009). Grasping familiar objects using shape context. In International conference on advanced robotics (pp. 1–6).
Bohren, J., Rusu, R.B., Gil Jones, E., Marder-Eppstein, E., Pantofaru, C., Wise, M., et al. (2011). Towards autonomous robotic butlers: Lessons learned with the PR2. In IEEE international conference on robotics and automation (pp. 5568–5575).
Boularias, A., Kroemer, O., & Peters, J. (2011). Learning robot grasping from 3-D images with Markov random fields. In IEEE/RSJ international conference on intelligent robots and systems (pp. 1548–1553).
Curtis, N., Xiao, J., & Member, S. (2008). Efficient and effective grasping of novel objects through learning and adapting a knowledge base. In IEEE international conference on robotics and automation (pp. 2252–2257).
Detry, R., Başeski, E., Popovic, M., Touati, Y., Krueger, N., Kroemer, O., et al. (2010). Learning continuous grasp affordances by sensorimotor exploration. In From motor learning to interaction learning in robots (pp. 451–465). Heidelberg: Springer.
Detry, R., Ek, C. H., Madry, M., & Kragic, D. (2013). Learning a dictionary of prototypical grasp-predicting parts from grasping experience. In IEEE international conference on robotics and automation.
Diankov, R., & Kuffner, J. (2008). Openrave: A planning architecture for autonomous robotics. Tech. Rep. CMU-RI-TR-08-34. Robotics Institute, Pittsburgh, PA.
Erkan, A., Kroemer, O., Detry, R., Altun, Y., Piater, J., & Peters, J. (2010). Learning probabilistic discriminative models of grasp affordances under limited supervision. In IEEE/RSJ international conference on intelligent robots and systems (pp. 1586–1591).
Ferrari, C., & Canny, J. (1992). Planning optimal grasps. In IEEE international conference on robotics and automation (pp. 2290–2295).
Goldfeder, C., & Allen, P. (2011). Data-driven grasping. Autonomous Robots, 31, 1–20.
Goldfeder, C., Allen, P. K., Lackner, C., & Pelossof, R. (2007). Grasp planning via decomposition trees. In IEEE international conference on robotics and automation (pp. 4679–4684).
Herzog, A., Pastor, P., Kalakrishnan, M., Righetti, L., Asfour, T., & Schaal, S. (2012). Template-based learning of grasp selection. In IEEE international conference on robotics and automation (pp. 2379–2384).
Hsiao, K., Chitta, S., Ciocarlie, M., & Jones, E. G. (2010). Contact-reactive grasping of objects with partial shape information. In IEEE/RSJ international conference on intelligent robots and systems (pp. 1228–1235).
Huebner, K., Welke, K., Przybylski, M., Vahrenkamp, N., Asfour, T., et al. (2009). Grasping known objects with humanoid robots: A box-based approach. In International conference on advanced robotics (pp. 1–6).
Kalakrishnan, M., Buchli, J., Pastor, P., & Schaal, S. (2009). Learning locomotion over rough terrain using terrain templates. In IEEE/RSJ international conference on intelligent robots and systems (pp. 167–172).
Klingbeil, E., Rao, D., Carpenter, B., Ganapathi, V., Ng, A. Y., & Khatib, O. (2011). Grasping with application to an autonomous checkout robot. In IEEE international conference on robotics and automation (pp. 2837–2844).
Kroemer, O., Ugur, E., Oztop, E., & Peters, J. (2012). A kernel-based approach to direct action perception. In IEEE international conference on robotics and automation (pp. 2605–2610).
Leon, B., Ulbrich, S., Diankov, R., Puche, G., Przybylski, M., Morales, A., et al. (2010). Opengrasp: A toolkit for robot grasping simulation. In 2nd international conference on simulation, modeling, and programming for autonomous robots.
Miller, A. T., & Allen, P. K. (2004). Graspit! A versatile simulator for robotic grasping. IEEE Robotics & Automation Magazine, 11(4), 110–122.
Miller, A. T., Knoop, S., Christensen, H. I., & Allen, P. K. (2003). Automatic grasp planning using shape primitives. In IEEE international conference on robotics and automation (pp. 1824–1829).
Montesano, L., & Lopes, M. (2009). Learning grasping affordances from local visual descriptors. In IEEE international conference on development and learning (pp. 1–6).
Papazov, C., Haddadin, S., Parusel, S., Krieger, K., & Burschka, D. (2012). Rigid 3D geometry matching for grasping of known objects in cluttered scenes. The International Journal of Robotics Research, 31(4), 538–553.
Popović, M., Kootstra, G., Jørgensen, J. A., Kragic, D., & Krüger, N. (2011). Grasping unknown objects using an early cognitive vision system for general scene understanding. In IEEE/RSJ international conference on intelligent robots and systems (pp. 987–994).
Przybylski, M., & Asfour, T. (2010). Unions of balls for shape approximation in robot grasping. In IEEE/RSJ international conference on intelligent robots and systems (pp. 1592–1599).
Ratliff, N., Bagnell, J., & Srinivasa, S. S. (2007). Imitation learning for locomotion and manipulation. In IEEE-RAS international conference on humanoid robots (pp. 392–397).
Richtsfeld, M., & Zillich, M. (2008). Grasping unknown objects based on 2.5D range data. In Proceedings of IEEE international conference on automation science and engineering CASE (pp. 691–696).
Righetti, L., Kalakrishnan, M., Pastor, P., Binney, J., Kelly, J., Voorhies, R., et al. (2013). An autonomous manipulation system based on force control and optimization. Autonomous Robots Journal Special Issue: Autonomous Grasping and Manipulation. doi:10.1007/s10514-013-9365-9.
Rubio, O., Huebner, K., & Kragic, D. (2010). Representations for object grasping and learning from experience. In IEEE/RSJ international conference on intelligent robots and systems (pp. 1566–1571).
Rusu, R. B., & Cousins, S. (2011). 3D is here: Point Cloud Library (PCL). In IEEE international conference on robotics and automation (pp. 1–4).
Saxena, A., Driemeyer, J., & Ng, A. Y. (2008). Robotic grasping of novel objects using vision. The International Journal of Robotics Research, 27(2), 157–173.
Saxena, A., Wong, L., & Ng, A. Y. (2008). Learning grasp strategies with partial shape information. In AAAI conference on artificial intelligence (pp. 1491–1494).
Stark, M., Lies, P., Zillich, M., Wyatt, J., & Schiele, B. (2008). Functional object class detection based on learned affordance cues. In International conference on computer vision systems. LNAI (Vol. 5008, pp. 435–444). Heidelberg: Springer.
Stückler, J., Steffens, R., Holz, D., & Behnke, S. (2011). Real-time 3D perception and efficient grasp planning for everyday manipulation tasks. In European conference on mobile robots (ECMR). Örebro, Sweden.
Ulbrich, S., Kappler, D., Asfour, T., Vahrenkamp, N., Bierbaum, A., et al. (2011). The OpenGRASP benchmarking suite: An environment for the comparative analysis of grasping and dexterous manipulation. In IEEE/RSJ international conference on intelligent robots and systems (pp. 1761–1767).
Acknowledgments
The work described in this paper was partially conducted within the EU Cognitive Systems project GRASP (IST-FP7-IP-215821) funded by the European Commission and the German Humanoid Research project SFB588 funded by the German Research Foundation (DFG: Deutsche Forschungsgemeinschaft). Alexander Herzog received support from the InterACT - International Center for Advanced Communication Technologies. This research was supported in part by National Science Foundation grants ECS-0326095, IIS-0535282, IIS-1017134, CNS-0619937, IIS-0917318, CBET-0922784, EECS-0926052, CNS-0960061, the DARPA program on Autonomous Robotic Manipulation, the Army Research Office, the Okawa Foundation, the ATR Computational Neuroscience Laboratories, and the Max-Planck-Society.
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Herzog, A., Pastor, P., Kalakrishnan, M. et al. Learning of grasp selection based on shape-templates. Auton Robot 36, 51–65 (2014). https://doi.org/10.1007/s10514-013-9366-8
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DOI: https://doi.org/10.1007/s10514-013-9366-8