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Push-manipulation of complex passive mobile objects using experimentally acquired motion models

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Abstract

In a realistic mobile push-manipulation scenario, it becomes non-trivial and infeasible to build analytical models that will capture the complexity of the interactions between the environment, each of the objects, and the robot as the variety of objects to be manipulated increases. We present an experience-based push-manipulation approach that enables the robot to acquire experimental models regarding how pushable real world objects with complex 3D structures move in response to various pushing actions. These experimentally acquired models can then be used either (1) for trying to track a collision-free guideline path generated for the object by reiterating pushing actions that result in the best locally-matching object trajectories until the goal is reached, or (2) as building blocks for constructing achievable push plans via a Rapidly-exploring Random Trees variant planning algorithm we contribute and executing them by reiterating the corresponding trajectories. We extensively experiment with these two methods in a 3D simulation environment and demonstrate the superiority of the achievable planning and execution concept through safe and successful push-manipulation of a variety of passively mobile pushable objects. Additionally, our preliminary tests in a real world scenario, where the robot is asked to arrange a set of chairs around a table through achievable push-manipulation, also show promising results despite the increased perception and action uncertainty, and verify the validity of our contributed method.

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Notes

  1. A video showing the robot performing experience-based reactive push-manipulation in simulation can be seen here: http://youtu.be/1Z8KW7fPGrA.

  2. A video showing the simulated mobile manipulator successfully push-manipulating an overbed table can be seen here: http://youtu.be/mw-PCjPa0XI.

  3. A video showing the physical CoBot acquiring and using push-manipulation cases in a real world setup can be seen here: http://youtu.be/TORQdBPHJ3g.

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Acknowledgments

We are grateful to Joydeep Biswas, Brian Coltin, and Stephanie Rosenthal for their work with the CoBot’s autonomy and task planning. We further thank Çetin Meriçli for his comments on this work and his help with the experiments with CoBot. The first author was partly supported by The Scientific and Technological Research Council of Turkey under Programmes 2211 and 2214, and the Turkish State Planning Organization (DPT) under the TAM Project, number 2007K120610. This research was further supported by the National Science Foundation under grant number IIS-1012733, by the Office of Naval Research under grant number N00014-09-1-1031, and by the Air Force Research Laboratory under grant number FA87501220291. The views and conclusions contained herein are those of the authors only.

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Authors and Affiliations

  1. Department of Computer Engineering, Boğaziçi University, Bebek, 34342, Istanbul, Turkey

    Tekin Meriçli & H. Levent Akın

  2. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Manuela Veloso

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  1. Tekin Meriçli
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  2. Manuela Veloso
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  3. H. Levent Akın
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Correspondence to Tekin Meriçli.

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Meriçli, T., Veloso, M. & Akın, H.L. Push-manipulation of complex passive mobile objects using experimentally acquired motion models. Auton Robot 38, 317–329 (2015). https://doi.org/10.1007/s10514-014-9414-z

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  • DOI: https://doi.org/10.1007/s10514-014-9414-z

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