Search Results for author: Jean-Jacques Slotine

Found 12 papers, 3 papers with code

Control-oriented meta-learning

1 code implementation14 Apr 2022 Spencer M. Richards, Navid Azizan, Jean-Jacques Slotine, Marco Pavone

Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments.

Meta-Learning

A Theoretical Overview of Neural Contraction Metrics for Learning-based Control with Guaranteed Stability

no code implementations2 Oct 2021 Hiroyasu Tsukamoto, Soon-Jo Chung, Jean-Jacques Slotine, Chuchu Fan

This paper presents a theoretical overview of a Neural Contraction Metric (NCM): a neural network model of an optimal contraction metric and corresponding differential Lyapunov function, the existence of which is a necessary and sufficient condition for incremental exponential stability of non-autonomous nonlinear system trajectories.

Recursive Construction of Stable Assemblies of Recurrent Neural Networks

no code implementations16 Jun 2021 Leo Kozachkov, Michaela Ennis, Jean-Jacques Slotine

Advanced applications of modern machine learning will likely involve combinations of trained networks, as are already used in spectacular systems such as DeepMind's AlphaGo.

Dynamical Pose Estimation

1 code implementation ICCV 2021 Heng Yang, Chris Doran, Jean-Jacques Slotine

We study the problem of aligning two sets of 3D geometric primitives given known correspondences.

Point Cloud Registration Pose Estimation

Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems

1 code implementation7 Mar 2021 Spencer M. Richards, Navid Azizan, Jean-Jacques Slotine, Marco Pavone

Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments.

Meta-Learning

Learning-based Adaptive Control using Contraction Theory

no code implementations4 Mar 2021 Hiroyasu Tsukamoto, Soon-Jo Chung, Jean-Jacques Slotine

Adaptive control is subject to stability and performance issues when a learned model is used to enhance its performance.

Ode to an ODE

no code implementations NeurIPS 2020 Krzysztof M. Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani

We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d).

An Ode to an ODE

no code implementations NeurIPS 2020 Krzysztof Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani

We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d).

Time Dependence in Non-Autonomous Neural ODEs

no code implementations ICLR Workshop DeepDiffEq 2019 Jared Quincy Davis, Krzysztof Choromanski, Jake Varley, Honglak Lee, Jean-Jacques Slotine, Valerii Likhosterov, Adrian Weller, Ameesh Makadia, Vikas Sindhwani

Neural Ordinary Differential Equations (ODEs) are elegant reinterpretations of deep networks where continuous time can replace the discrete notion of depth, ODE solvers perform forward propagation, and the adjoint method enables efficient, constant memory backpropagation.

Image Classification Video Prediction

Notes on stable learning with piecewise-linear basis functions

no code implementations25 Apr 2018 Winfried Lohmiller, Philipp Gassert, Jean-Jacques Slotine

We discuss technical results on learning function approximations using piecewise-linear basis functions, and analyze their stability and convergence using nonlinear contraction theory.

A Quorum Sensing Inspired Algorithm for Dynamic Clustering

no code implementations16 Mar 2013 Feng Tan, Jean-Jacques Slotine

The algorithm treats each data as a single cell, and uses knowledge of local connectivity to cluster cells into multiple colonies simultaneously.

Community Detection Semantic Segmentation

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