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Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks
Rearranging and manipulating deformable objects such as cables, fabrics,...
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Safely Learning Dynamical Systems from Short Trajectories
A fundamental challenge in learning to control an unknown dynamical syst...
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Transporter Networks: Rearranging the Visual World for Robotic Manipulation
Robotic manipulation can be formulated as inducing a sequence of spatial...
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Piecewise-Linear Motion Planning amidst Static, Moving, or Morphing Obstacles
We propose a novel method for planning shortest length piecewise-linear ...
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Learning Stability Certificates from Data
Many existing tools in nonlinear control theory for establishing stabili...
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An Ode to an ODE
We present a new paradigm for Neural ODE algorithms, calledODEtoODE, whe...
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Time Dependence in Non-Autonomous Neural ODEs
Neural Ordinary Differential Equations (ODEs) are elegant reinterpretati...
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Robotic Table Tennis with Model-Free Reinforcement Learning
We propose a model-free algorithm for learning efficient policies capabl...
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Stochastic Flows and Geometric Optimization on the Orthogonal Group
We present a new class of stochastic, geometrically-driven optimization ...
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Policies Modulating Trajectory Generators
We propose an architecture for learning complex controllable behaviors b...
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Learning Stabilizable Nonlinear Dynamics with Contraction-Based Regularization
We propose a novel framework for learning stabilizable nonlinear dynamic...
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Data Efficient Reinforcement Learning for Legged Robots
We present a model-based framework for robot locomotion that achieves wa...
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Teleoperator Imitation with Continuous-time Safety
Learning to effectively imitate human teleoperators, with generalization...
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Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces
We present a new algorithm ASEBO for conducting optimization of high-dim...
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When random search is not enough: Sample-Efficient and Noise-Robust Blackbox Optimization of RL Policies
Interest in derivative-free optimization (DFO) and "evolutionary strateg...
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Learning Stabilizable Dynamical Systems via Control Contraction Metrics
We propose a novel framework for learning stabilizable nonlinear dynamic...
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Optimizing Simulations with Noise-Tolerant Structured Exploration
We propose a simple drop-in noise-tolerant replacement for the standard ...
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Learning Contracting Vector Fields For Stable Imitation Learning
We propose a new non-parametric framework for learning incrementally sta...
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Structured Evolution with Compact Architectures for Scalable Policy Optimization
We present a new method of blackbox optimization via gradient approximat...
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Manifold Regularization for Kernelized LSTD
Policy evaluation or value function or Q-function approximation is a key...
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Geometry of 3D Environments and Sum of Squares Polynomials
Motivated by applications in robotics and computer vision, we study prob...
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Hierarchically Compositional Kernels for Scalable Nonparametric Learning
We propose a novel class of kernels to alleviate the high computational ...
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Recycling Randomness with Structure for Sublinear time Kernel Expansions
We propose a scheme for recycling Gaussian random vectors into structure...
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Learning Compact Recurrent Neural Networks
Recurrent neural networks (RNNs), including long short-term memory (LSTM...
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Structured Transforms for Small-Footprint Deep Learning
We consider the task of building compact deep learning pipelines suitabl...
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Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
We consider the problem of improving the efficiency of randomized Fourie...
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Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality
We propose a general matrix-valued multiple kernel learning framework fo...
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Near-separable Non-negative Matrix Factorization with ℓ_1- and Bregman Loss Functions
Recently, a family of tractable NMF algorithms have been proposed under ...
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Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization
The separability assumption (Donoho & Stodden, 2003; Arora et al., 2012)...
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Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization
We describe novel subgradient methods for a broad class of matrix optimi...
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