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Self-supervised damage-avoiding manipulation strategy optimization via mental simulation

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Abstract

Everyday robotics are challenged to deal with autonomous product handling in applications such as logistics or retail, possibly causing damage to the items during manipulation. Traditionally, most approaches try to minimize physical interaction with goods. However, this paper proposes to take into account any unintended object motion and to learn damage-minimizing manipulation strategies in a self-supervised way. The presented approach consists of a simulation-based planning method for an optimal manipulation sequence with respect to possible damage. The planned manipulation sequences are generalized to new, unseen scenes in the same application scenario using machine learning. This learned manipulation strategy is continuously refined in a self-supervised, simulation-in-the-loop optimization cycle during load-free times of the system, commonly known as mental simulation. In parallel, the generated manipulation strategies can be deployed in near-real time in an anytime fashion. The approach is validated on an industrial container-unloading scenario and on a retail shelf-replenishment scenario.

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Notes

  1. http://roblog.eu.

  2. http://gazebosim.org.

  3. http://ompl.kavrakilab.org/.

  4. https://github.com/Rasoul77/promts.

  5. The initial training set size should depend on the maximum tolerable time until the classifier is required for the first time.

  6. Convergence of the optimization method strongly depends on the concrete implementation and application scenario and is hard to define generically as explained in the evaluation section.

  7. https://docker.com.

  8. https://github.com/jacobs-robotics/gazebo-mental-simulation.

  9. https://tobias.doernba.ch/research/datasets/mental-simulation.

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Acknowledgements

The research leading to the presented results has received funding from the European Union’s Seventh Framework program (EU FP7 ICT-2) within the project “Cognitive Robot for Automation of Logistics Processes” (RobLog) under grant agreement No. 270350.

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  1. Robotics Group, Computer Science & Electrical Engineering, Jacobs University, Campus Ring 1, 28759, Bremen, Germany

    Tobias Doernbach

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Tobias Doernbach was formerly known as Tobias Fromm.

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Doernbach, T. Self-supervised damage-avoiding manipulation strategy optimization via mental simulation. Intel Serv Robotics 12, 333–357 (2019). https://doi.org/10.1007/s11370-019-00286-7

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