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Cleaning Tasks Knowledge Transfer Between Heterogeneous Robots: a Deep Learning Approach

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Abstract

Nowadays, autonomous service robots are becoming an important topic in robotic research. Differently from typical industrial scenarios, with highly controlled environments, service robots must show an additional robustness to task perturbations and changes in the characteristics of their sensory feedback. In this paper, a robot is taught to perform two different cleaning tasks over a table, using a learning from demonstration paradigm. However, differently from other approaches, a convolutional neural network is used to generalize the demonstrations to different, not yet seen dirt or stain patterns on the same table using only visual feedback, and to perform cleaning movements accordingly. Robustness to robot posture and illumination changes is achieved using data augmentation techniques and camera images transformation. This robustness allows the transfer of knowledge regarding execution of cleaning tasks between heterogeneous robots operating in different environmental settings. To demonstrate the viability of the proposed approach, a network trained in Lisbon to perform cleaning tasks, using the iCub robot, is successfully employed by the DoRo robot in Peccioli, Italy.

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Acknowledgements

This work was partially supported by Fundação para a Ciência e a Tecnologia (project UID/EEA/50009/2013 and Grant PD/BD/135115/2017) and the RBCog-Lab research infrastructure. We acknowledge the support of NVIDIA Corporation with the donation of the GPU used for this research.

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Authors and Affiliations

  1. BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy

    Jaeseok Kim & Filippo Cavallo

  2. Institute for Systems and Robotics, Instituto Superior Tecnico, Universidade de Lisboa, Lisbon, Portugal

    Nino Cauli, Pedro Vicente, Bruno Damas, Alexandre Bernardino & José Santos-Victor

  3. CINAV — Centro de Investigação Naval, Almada, Portugal

    Bruno Damas

Authors
  1. Jaeseok Kim
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  2. Nino Cauli
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  3. Pedro Vicente
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  4. Bruno Damas
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  5. Alexandre Bernardino
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  6. José Santos-Victor
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  7. Filippo Cavallo
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Correspondence to Jaeseok Kim.

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Parts of this manuscript were previously presented at the IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC 2018), Torres Vedras

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Kim, J., Cauli, N., Vicente, P. et al. Cleaning Tasks Knowledge Transfer Between Heterogeneous Robots: a Deep Learning Approach. J Intell Robot Syst 98, 191–205 (2020). https://doi.org/10.1007/s10846-019-01072-4

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