Abstract
Optimal control in robotics has been increasingly popular in recent years and has been applied in many applications involving complex dynamical systems. Closed-loop optimal control strategies include model predictive control (MPC) and time-varying linear controllers optimized through iLQR. However, such feedback controllers rely on the information of the current state, limiting the range of robotic applications where the robot needs to remember what it has done before to act and plan accordingly. The recently proposed system level synthesis (SLS) framework circumvents this limitation via a richer controller structure with memory. In this work, we propose to optimally design reactive anticipatory robot skills with memory by extending SLS to tracking problems involving nonlinear systems and nonquadratic cost functions. We showcase our method with two scenarios exploiting task precisions and object affordances in pick-and-place tasks in a simulated and a real environment with a 7-axis Franka Emika robot.
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Acknowledgements
We would like to thank the reviewers for their thoughtful remarks and suggestions. This work was supported in part by the European Commission’s Horizon 2020 Programme through the CoLLaboratE project (https://collaborate-project.eu/) under grant agreement 820767.
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Girgin, H., Jankowski, J., Calinon, S. (2023). Reactive Anticipatory Robot Skills with Memory. In: Billard, A., Asfour, T., Khatib, O. (eds) Robotics Research. ISRR 2022. Springer Proceedings in Advanced Robotics, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-031-25555-7_30
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DOI: https://doi.org/10.1007/978-3-031-25555-7_30
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