Abstract
This book has focused on a complete framework for learning motion tasks in mostly unmodelled environments with robotic arms. We devised strategies to compliantly learn such tasks both in the joint space and in the robot’s operational—or Cartesian- space, as well as to obtain coordination schemes for the robot’s DoF for a certain task.
In the twenty-first century, the robot will take the place which slave labor occupied in ancient civilization.
Nikola Tesla, 1935
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References
Hansen, N.: The CMA Evolution Strategy: A Comparing Review, pp. 75–102. Springer, Berlin (2006)
Jevtic, A., Colomé, A., Alenya, G., Torras, C.: User evaluation of an interactive learning framework for single-arm and dual-arm robots. In: 8th International Conference on Social Robotics, pp. 52–61 (2016)
Jevtic, A., Colomé, A., Alenya, G., Torras, C.: Robot motion adaptation through user intervention and reinforcement learning. Pattern Recognit. Lett. 105, 67–75 (2018)
Rozo, L., Silvério, J., Calinon, S., Caldwell, D.G.: Learning controllers for reactive and proactive behaviors in human-robot collaboration. Front. Robot. AI 3(30), 1–11 (2016)
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Colomé, A., Torras, C. (2020). Conclusions. In: Reinforcement Learning of Bimanual Robot Skills. Springer Tracts in Advanced Robotics, vol 134. Springer, Cham. https://doi.org/10.1007/978-3-030-26326-3_9
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DOI: https://doi.org/10.1007/978-3-030-26326-3_9
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