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pro vyhledávání: '"Jallet, Wilson"'
Recent strides in nonlinear model predictive control (NMPC) underscore a dependence on numerical advancements to efficiently and accurately solve large-scale problems. Given the substantial number of variables characterizing typical whole-body optima
Externí odkaz:
http://arxiv.org/abs/2405.09197
Autor:
Lidec, Quentin Le, Jallet, Wilson, Montaut, Louis, Laptev, Ivan, Schmid, Cordelia, Carpentier, Justin
Physics simulation is ubiquitous in robotics. Whether in model-based approaches (e.g., trajectory optimization), or model-free algorithms (e.g., reinforcement learning), physics simulators are a central component of modern control pipelines in roboti
Externí odkaz:
http://arxiv.org/abs/2304.06372
Publikováno v:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct 2022, Kyoto, Japan
Trajectory optimization is an efficient approach for solving optimal control problems for complex robotic systems. It relies on two key components: first the transcription into a sparse nonlinear program, and second the corresponding solver to iterat
Externí odkaz:
http://arxiv.org/abs/2210.15409
Publikováno v:
6th Legged Robots Workshop, May 2022, Philadelphia, Pennsylvania, United States
Mathematical optimization is the workhorse behind several aspects of modern robotics and control. In these applications, the focus is on constrained optimization, and the ability to work on manifolds (such as the classical matrix Lie groups), along w
Externí odkaz:
http://arxiv.org/abs/2210.02109
Reinforcement learning (RL) and trajectory optimization (TO) present strong complementary advantages. On one hand, RL approaches are able to learn global control policies directly from data, but generally require large sample sizes to properly conver
Externí odkaz:
http://arxiv.org/abs/2209.09006
Akademický článek
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Publikováno v:
International Conference on Robotics and Automation
International Conference on Robotics and Automation, May 2023, London, United Kingdom
2023 International Conference on Robotics and Automation (ICRA)
ICRA 2023-IEEE International Conference on Robotics and Automation
ICRA 2023-IEEE International Conference on Robotics and Automation, May 2023, London, United Kingdom
International Conference on Robotics and Automation, May 2023, London, United Kingdom
2023 International Conference on Robotics and Automation (ICRA)
ICRA 2023-IEEE International Conference on Robotics and Automation
ICRA 2023-IEEE International Conference on Robotics and Automation, May 2023, London, United Kingdom
International audience; Reinforcement learning (RL) and trajectory optimization (TO) present strong complementary advantages. On one hand, RL approaches are able to learn global control policies directly from data, but generally require large sample
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b1bfffa24fd4b78c4afc085a4a24e5fb
https://hal.science/hal-03780392/document
https://hal.science/hal-03780392/document