Abstract
In this work, we address the problem of symmetry transfer in human-robot collaborative tasks, i.e., how certain actions can be extended to their symmetrical by exploiting symmetries in their execution. We contribute an approach capable of considering the symmetry inherent to a given task, such as the human or robot’s lateral symmetry, abstracting them from the robot’s decision process. We instantiate our approach in a robot-assisted backpack dressing scenario. A two-manipulator Baxter robot assists a human user in sequentially putting on both straps of a backpack. We evaluate the proposed symmetry-transfer approach in two complementary perspectives: the quality of the agent’s learned policy in a simulated environment and the efficiency of the complete system in a real-life scenario with a robotic platform. The results show that our approach allows the extension of the execution of single-side trained collaborative tasks to their symmetrical with no additional training and minimal performance loss.
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Acknowledgements
This work was partially supported by national funds through the Portuguese Fundação para a Ciência e a Tecnologia under project UIDB/50021/2020 (INESC-ID multi-annual funding) and the Carnegie Mellon Portugal Program and its Information and Communications Technologies Institute, under project CMUP-ERI/HCI/0051/2013. R. Silva acknowledges the PhD grant SFRH/BD/113695/ 2015. M. Vasco acknowledges the PhD grant SFRH/BD/139362/2018.
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Ildefonso, P. et al. (2021). Exploiting Symmetry in Human Robot-Assisted Dressing Using Reinforcement Learning. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science(), vol 12981. Springer, Cham. https://doi.org/10.1007/978-3-030-86230-5_32
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