Abstract
Physics engines have been used in robotics research for a long time. Beside their traditional application as a substitute for real world interactions due to their higher speed, safety and flexibility, they have recently also been used for motion planning and high level action planning. We propose to further explore the idea of using a physics engine as means to give a robot a basic physical understanding of its environment. In this paper, as a preliminary step, we study, how accurately the process of pushing flat objects across a table with a robot arm can be predicted in a physics engine. We also present an approach to adapt the engines parameters to enhance the simulation accuracy.
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References
Baillargeon, R.: How do infants learn about the physical world? Current Directions in Psychological Science 3, 133–140 (1994)
Forbus, K.D.: Qualitative process theory. Artificial intelligence 24, 85–168 (1984)
Kuipers, B.: Qualitative simulation. Artificial intelligence 29, 289–338 (1986)
Berleant, D., Kuipers, B.J.: Qualitative and quantitative simulation: bridging the gap. Artificial Intelligence 95, 215–255 (1997)
Lipson, H., Pollack, J.: Evolving physical creatures. In: Artificial Life VII: Proceedings of the Seventh International Conference on Artificial life, pp. 282–287. MIT Press, Cambridge (2000)
Sucan, I., Kavraki, L.: Kinodynamic Motion Planning by Interior-Exterior Cell Exploration. In: Intl. Workshop on the Algorithmic Foundations of Robotics, Guanajuato, Mexico (2008)
Pasula, H., Zettlemoyer, L., Kaelbling, L.: Learning symbolic models of stochastic domains. Journal of Artificial Intelligence Research 29, 309–352 (2007)
Boeing, A., Braeunl, T.: Evaluation of real-time physics simulation systems. In: GRAPHITE, Perth, Western Australia (2007)
Nelder, J., Mead, R.: A simplex method for function minimization. The Computer Journal 7, 308 (1965)
Storn, R., Price, K.: Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces. In: International Computer Science Institute – publications (1995)
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Weitnauer, E., Haschke, R., Ritter, H. (2010). Evaluating a Physics Engine as an Ingredient for Physical Reasoning. In: Ando, N., Balakirsky, S., Hemker, T., Reggiani, M., von Stryk, O. (eds) Simulation, Modeling, and Programming for Autonomous Robots. SIMPAR 2010. Lecture Notes in Computer Science(), vol 6472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17319-6_16
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DOI: https://doi.org/10.1007/978-3-642-17319-6_16
Publisher Name: Springer, Berlin, Heidelberg
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