no code implementations • 22 Dec 2023 • Patrick M. Wensing, Johannes Englsberger, Jean-Jacques E. Slotine
Many energy-based control strategies for mechanical systems require the choice of a Coriolis factorization satisfying a skew-symmetry property.
no code implementations • 20 Dec 2021 • Thomas T. C. K. Zhang, Stephen Tu, Nicholas M. Boffi, Jean-Jacques E. Slotine, Nikolai Matni
Motivated by bridging the simulation to reality gap in the context of safety-critical systems, we consider learning adversarially robust stability certificates for unknown nonlinear dynamical systems.
no code implementations • 1 Oct 2021 • Hiroyasu Tsukamoto, Soon-Jo Chung, Jean-Jacques E. Slotine
Contraction theory is an analytical tool to study differential dynamics of a non-autonomous (i. e., time-varying) nonlinear system under a contraction metric defined with a uniformly positive definite matrix, the existence of which results in a necessary and sufficient characterization of incremental exponential stability of multiple solution trajectories with respect to each other.
no code implementations • 7 Jun 2021 • Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
A key assumption in the theory of nonlinear adaptive control is that the uncertainty of the system can be expressed in the linear span of a set of known basis functions.
no code implementations • 6 Apr 2021 • Brett T. Lopez, Jean-Jacques E. Slotine
The stable combination of optimal feedback policies with online learning is studied in a new control-theoretic framework for uncertain nonlinear systems.
no code implementations • 31 Dec 2020 • Brett T. Lopez, Jean-Jacques E. Slotine
This work develops a new direct adaptive control framework that extends the certainty equivalence principle to general nonlinear systems with unmatched model uncertainties.
no code implementations • 26 Nov 2020 • Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
We study the problem of adaptively controlling a known discrete-time nonlinear system subject to unmodeled disturbances.
1 code implementation • 6 Nov 2020 • Hiroyasu Tsukamoto, Soon-Jo Chung, Jean-Jacques E. Slotine
We present Neural Stochastic Contraction Metrics (NSCM), a new design framework for provably-stable robust control and estimation for a class of stochastic nonlinear systems.
no code implementations • 13 Aug 2020 • Nicholas M. Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques E. Slotine, Vikas Sindhwani
Many existing tools in nonlinear control theory for establishing stability or safety of a dynamical system can be distilled to the construction of a certificate function that guarantees a desired property.
no code implementations • 15 Jun 2020 • Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
Recent numerical experiments have demonstrated that the choice of optimization geometry used during training can impact generalization performance when learning expressive nonlinear model classes such as deep neural networks.
no code implementations • 31 Dec 2019 • Nicholas M. Boffi, Jean-Jacques E. Slotine
Stable concurrent learning and control of dynamical systems is the subject of adaptive control.
1 code implementation • 29 Jul 2019 • Sumeet Singh, Spencer M. Richards, Vikas Sindhwani, Jean-Jacques E. Slotine, Marco Pavone
We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics.
no code implementations • 28 Dec 2018 • Nicholas M. Boffi, Jean-Jacques E. Slotine
We analyze the effect of synchronization on distributed stochastic gradient algorithms.
no code implementations • 31 Jul 2018 • Sumeet Singh, Vikas Sindhwani, Jean-Jacques E. Slotine, Marco Pavone
We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics.
3 code implementations • 10 Nov 2017 • Patrick M. Wensing, Günter Niemeyer, Jean-Jacques E. Slotine
This paper presents an algorithm to geometrically characterize inertial parameter identifiability for an articulated robot.
Robotics