On Convex Data-Driven Inverse Optimal Control for Nonlinear, Non-stationary and Stochastic Systems
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866917706128687104 |
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| author | Garrabe, Emiland Jesawada, Hozefa Del Vecchio, Carmen Russo, Giovanni |
| author_facet | Garrabe, Emiland Jesawada, Hozefa Del Vecchio, Carmen Russo, Giovanni |
| contents | This paper is concerned with a finite-horizon inverse control problem, which has the goal of reconstructing, from observations, the possibly non-convex and non-stationary cost driving the actions of an agent. In this context, we present a result enabling cost reconstruction by solving an optimization problem that is convex even when the agent cost is not and when the underlying dynamics is nonlinear, non-stationary and stochastic. To obtain this result, we also study a finite-horizon forward control problem that has randomized policies as decision variables. We turn our findings into algorithmic procedures and show the effectiveness of our approach via in-silico and hardware validations. All experiments confirm the effectiveness of our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_13928 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | On Convex Data-Driven Inverse Optimal Control for Nonlinear, Non-stationary and Stochastic Systems Garrabe, Emiland Jesawada, Hozefa Del Vecchio, Carmen Russo, Giovanni Optimization and Control Information Theory Machine Learning Robotics Dynamical Systems This paper is concerned with a finite-horizon inverse control problem, which has the goal of reconstructing, from observations, the possibly non-convex and non-stationary cost driving the actions of an agent. In this context, we present a result enabling cost reconstruction by solving an optimization problem that is convex even when the agent cost is not and when the underlying dynamics is nonlinear, non-stationary and stochastic. To obtain this result, we also study a finite-horizon forward control problem that has randomized policies as decision variables. We turn our findings into algorithmic procedures and show the effectiveness of our approach via in-silico and hardware validations. All experiments confirm the effectiveness of our approach. |
| title | On Convex Data-Driven Inverse Optimal Control for Nonlinear, Non-stationary and Stochastic Systems |
| topic | Optimization and Control Information Theory Machine Learning Robotics Dynamical Systems |
| url | https://arxiv.org/abs/2306.13928 |