Abstract
We present an approach for learning a mapping between geometric states and logical predicates. This mapping is a necessary part of any robotic system that requires task-level reasoning and path planning. Consider a robot tasked with putting a number of cups on a tray. To achieve the goal the robot needs to find positions for all the objects, and if necessary may need to stack one cup inside another to get them all on the tray. This requires translating back and forth between symbolic states that the planner uses such as “stacked(cup1,cup2)” and geometric states representing the positions and poses of the objects. The mapping we learn in this paper achieves this translation. We learn it from labelled examples, and significantly, learn a representation that can be used in both the forward (from geometric to symbolic) and reverse directions. This enables us to build symbolic representations of scenes the robot observes, and also to translate a desired symbolic state from a plan into a geometric state that the robot can actually achieve through manipulation. We also show how the approach can be used to generate significantly different geometric solutions to support backtracking. We evaluate the work both in simulation and on a robot arm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blum, A., Furst, M.: Fast planning through planning graph analysis. Artificial Intelligence (90), 281–300 (1997)
Helmert, M.: The Fast Downward planning system. Journal of Artificial Intelligence Research 26, 191–246 (2006)
McDermott, D., Ghallab, M., Howe, A., Knoblock, C.A., Ram, A., Veloso, M., Weld, D.S., Wilkins, D.E.: PDDL—The planning domain definition language. Technical Report DCS TR-1165. Yale University, New Haven, Connecticut (1998)
James, J., Kuffner, J., LaValle, S.M.: RRT-connect: An efficient approach to single-query path planning. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2000), pp. 995–1001 (2000)
Siméon, T., Laumond, J.P., Nissoux, C.: Visibility based probabilistic roadmaps for motion planning. Advanced Robotics Journal (2000)
Scott, D.W.: Multivariate Density Estimation: Theory, Practice, and Visualization. John Wiley and Sons, Inc., Hoboken (2008)
Morariu, V.I., Srinivasan, B.V., Raykar, V.C., Duraiswami, R., Davis, L.S.: Automatic online tuning for fast Gaussian summation. In: Advances in Neural Information Processing Systems, NIPS (2008)
Gravot, F., Cambon, S., Alami, R.: asymov: A planner that deals with intricate symbolic and geometric problems. In: ISRR. Springer Tracts in Advanced Robotics, vol. 15, pp. 100–110. Springer (2003)
Kaelbling, L.P., Lozano-Pérez, T.: Hierarchical task and motion planning in the now. In: Proceedings of Robotics and Automation (ICRA), pp. 1470–1477 (2011)
Jäkel, R., Schmidt-Rohr, S., Lösch, M., Kasper, A., Dillmann, R.: Learning of generalized manipulation strategies in the context of programming by demonstration. In: 2010 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp. 542–547. IEEE (2010)
Sjöö, K., Aydemir, A., Morwald, T., Zhou, K., Jensfelt, P.: Mechanical support as a spatial abstraction for mobile robots. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4894–4900. IEEE (2010)
Sjöö, K., Jensfelt, P.: Learning spatial relations from functional simulation. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1513–1519. IEEE (2011)
Rosman, B., Ramamoorthy, S.: Learning spatial relationships between objects. The International Journal of Robotics Research 30(11), 1328–1342 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Burbridge, C., Dearden, R. (2012). Learning the Geometric Meaning of Symbolic Abstractions for Manipulation Planning. In: Herrmann, G., et al. Advances in Autonomous Robotics. TAROS 2012. Lecture Notes in Computer Science(), vol 7429. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32527-4_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-32527-4_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32526-7
Online ISBN: 978-3-642-32527-4
eBook Packages: Computer ScienceComputer Science (R0)