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Abstract / Description of output
Optimal control is a popular approach to synthesize highly dynamic motion. Commonly, L2 regularization is used on the control inputs in order to minimize energy used and to ensure smoothness of the control inputs. However, for some systems, such as satellites, the control needs to be applied in sparse bursts due to how the propulsion system operates. In this paper, we study approaches to induce sparsity in optimal control solutions—namely via smooth L1 and Huber regularization penalties. We apply these loss terms to state-of-the-art Differential Dynamic Programming (DDP)-based solvers to create a family of sparsity-inducing optimal control methods. We analyze and compare the effect of the different losses on inducing sparsity, their numerical conditioning, their impact on convergence, and discuss hyperparameter settings. We demonstrate our method in simulation and hardware experiments on canonical dynamics systems, control of satellites, and the NASA Valkyrie humanoid robot. We provide an implementation of our method and all examples for reproducibility on GitHub.
|Title of host publication||2021 IEEE International Conference on Robotics and Automation (ICRA)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||7|
|Publication status||Published - 18 Oct 2021|
|Event||2021 IEEE International Conference on Robotics and Automation - Xi'an, China|
Duration: 30 May 2021 → 5 Jun 2021
|Conference||2021 IEEE International Conference on Robotics and Automation|
|Abbreviated title||ICRA 2021|
|Period||30/05/21 → 5/06/21|
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