no code implementations • 9 Dec 2023 • Motoya Ohnishi, Iretiayo Akinola, Jie Xu, Ajay Mandlekar, Fabio Ramos
As a specific case of our framework, we devise a model predictive control method for path tracking.
no code implementations • 2 Jun 2022 • Motoya Ohnishi, Isao Ishikawa, Yuko Kuroki, Masahiro Ikeda
This work present novel method for structure estimation of an underlying dynamical system.
1 code implementation • 30 Jun 2021 • Motoya Ohnishi, Isao Ishikawa, Kendall Lowrey, Masahiro Ikeda, Sham Kakade, Yoshinobu Kawahara
In this work, we present a novel paradigm of controlling nonlinear systems via the minimization of the Koopman spectrum cost: a cost over the Koopman operator of the controlled dynamics.
1 code implementation • NeurIPS 2020 • Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun
This work studies the problem of sequential control in an unknown, nonlinear dynamical system, where we model the underlying system dynamics as an unknown function in a known Reproducing Kernel Hilbert Space.
no code implementations • 26 Aug 2019 • Motoya Ohnishi, Gennaro Notomista, Masashi Sugiyama, Magnus Egerstedt
When deploying autonomous agents in unstructured environments over sustained periods of time, adaptability and robustness oftentimes outweigh optimality as a primary consideration.
no code implementations • NeurIPS 2018 • Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama
Motivated by the success of reinforcement learning (RL) for discrete-time tasks such as AlphaGo and Atari games, there has been a recent surge of interest in using RL for continuous-time control of physical systems (cf.
no code implementations • 29 Jan 2018 • Motoya Ohnishi, Li Wang, Gennaro Notomista, Magnus Egerstedt
This paper presents a safe learning framework that employs an adaptive model learning algorithm together with barrier certificates for systems with possibly nonstationary agent dynamics.