Vikas Sindhwani

research

∙ 05/24/2023

Barkour: Benchmarking Animal-level Agility with Quadruped Robots

Animals have evolved various agile locomotion strategies, such as sprint...

2 Ken Caluwaerts, et al. ∙

research

∙ 05/20/2023

Safely Learning Dynamical Systems

A fundamental challenge in learning an unknown dynamical system is to re...

0 Amir Ali Ahmadi, et al. ∙

research

∙ 02/02/2023

Mnemosyne: Learning to Train Transformers with Transformers

Training complex machine learning (ML) architectures requires a compute ...

0 Deepali Jain, et al. ∙

research

∙ 12/13/2022

Single-Level Differentiable Contact Simulation

We present a differentiable formulation of rigid-body contact dynamics f...

0 Simon Le Cleac'h, et al. ∙

research

∙ 11/29/2022

A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations

Object-goal navigation (Object-nav) entails searching, recognizing and n...

1 Sohan Rudra, et al. ∙

research

∙ 10/19/2022

Robotic Table Wiping via Reinforcement Learning and Whole-body Trajectory Optimization

We propose a framework to enable multipurpose assistive mobile robots to...

5 Thomas Lew, et al. ∙

research

∙ 09/22/2022

Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation

Despite decades of research, existing navigation systems still face real...

16 Xuesu Xiao, et al. ∙

research

∙ 08/02/2022

Implicit Two-Tower Policies

We present a new class of structured reinforcement learning policy-archi...

0 Yunfan Zhao, et al. ∙

research

∙ 04/01/2022

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

Large foundation models can exhibit unique capabilities depending on the...

1 Andy Zeng, et al. ∙

research

∙ 03/16/2022

Multiscale Sensor Fusion and Continuous Control with Neural CDEs

Though robot learning is often formulated in terms of discrete-time Mark...

5 Sumeet Singh, et al. ∙

research

∙ 10/08/2021

Hybrid Random Features

We propose a new class of random feature methods for linearizing softmax...

0 Krzysztof Choromanski, et al. ∙

research

∙ 09/10/2021

Trajectory Optimization with Optimization-Based Dynamics

We present a framework for bi-level trajectory optimization in which a s...

0 Taylor A. Howell, et al. ∙

research

∙ 12/06/2020

Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks

Rearranging and manipulating deformable objects such as cables, fabrics,...

7 Daniel Seita, et al. ∙

research

∙ 11/24/2020

Safely Learning Dynamical Systems from Short Trajectories

A fundamental challenge in learning to control an unknown dynamical syst...

0 Amir Ali Ahmadi, et al. ∙

research

∙ 10/27/2020

Transporter Networks: Rearranging the Visual World for Robotic Manipulation

Robotic manipulation can be formulated as inducing a sequence of spatial...

0 Andy Zeng, et al. ∙

research

∙ 10/16/2020

Piecewise-Linear Motion Planning amidst Static, Moving, or Morphing Obstacles

We propose a novel method for planning shortest length piecewise-linear ...

0 Bachir El Khadir, et al. ∙

research

∙ 08/13/2020

Learning Stability Certificates from Data

Many existing tools in nonlinear control theory for establishing stabili...

2 Nicholas M. Boffi, et al. ∙

research

∙ 06/19/2020

An Ode to an ODE

We present a new paradigm for Neural ODE algorithms, calledODEtoODE, whe...

18 Krzysztof Choromanski, et al. ∙

research

∙ 05/05/2020

Time Dependence in Non-Autonomous Neural ODEs

Neural Ordinary Differential Equations (ODEs) are elegant reinterpretati...

7 Jared Quincy Davis, et al. ∙

research

∙ 03/31/2020

Robotic Table Tennis with Model-Free Reinforcement Learning

We propose a model-free algorithm for learning efficient policies capabl...

18 Wenbo Gao, et al. ∙

research

∙ 03/30/2020

Stochastic Flows and Geometric Optimization on the Orthogonal Group

We present a new class of stochastic, geometrically-driven optimization ...

2 Krzysztof Choromanski, et al. ∙

research

∙ 10/07/2019

Policies Modulating Trajectory Generators

We propose an architecture for learning complex controllable behaviors b...

12 Atil Iscen, et al. ∙

research

∙ 07/29/2019

Learning Stabilizable Nonlinear Dynamics with Contraction-Based Regularization

We propose a novel framework for learning stabilizable nonlinear dynamic...

0 Sumeet Singh, et al. ∙

research

∙ 07/08/2019

Data Efficient Reinforcement Learning for Legged Robots

We present a model-based framework for robot locomotion that achieves wa...

6 Yuxiang Yang, et al. ∙

research

∙ 05/23/2019

Teleoperator Imitation with Continuous-time Safety

Learning to effectively imitate human teleoperators, with generalization...

0 Bachir El Khadir, et al. ∙

research

∙ 03/07/2019

Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces

We present a new algorithm ASEBO for conducting optimization of high-dim...

12 Krzysztof Choromanski, et al. ∙

research

∙ 03/07/2019

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...

12 Krzysztof Choromanski, et al. ∙

research

∙ 07/31/2018

Learning Stabilizable Dynamical Systems via Control Contraction Metrics

We propose a novel framework for learning stabilizable nonlinear dynamic...

0 Sumeet Singh, et al. ∙

research

∙ 05/20/2018

Optimizing Simulations with Noise-Tolerant Structured Exploration

We propose a simple drop-in noise-tolerant replacement for the standard ...

0 Krzysztof Choromanski, et al. ∙

research

∙ 04/13/2018

Learning Contracting Vector Fields For Stable Imitation Learning

We propose a new non-parametric framework for learning incrementally sta...

0 Vikas Sindhwani, et al. ∙

research

∙ 04/06/2018

Structured Evolution with Compact Architectures for Scalable Policy Optimization

We present a new method of blackbox optimization via gradient approximat...

0 Krzysztof Choromanski, et al. ∙

research

∙ 10/15/2017

Manifold Regularization for Kernelized LSTD

Policy evaluation or value function or Q-function approximation is a key...

0 Xinyan Yan, et al. ∙

research

∙ 11/22/2016

Geometry of 3D Environments and Sum of Squares Polynomials

Motivated by applications in robotics and computer vision, we study prob...

0 Amir Ali Ahmadi, et al. ∙

research

∙ 08/02/2016

Hierarchically Compositional Kernels for Scalable Nonparametric Learning

We propose a novel class of kernels to alleviate the high computational ...

0 Jie Chen, et al. ∙

research

∙ 05/29/2016

Recycling Randomness with Structure for Sublinear time Kernel Expansions

We propose a scheme for recycling Gaussian random vectors into structure...

0 Krzysztof Choromanski, et al. ∙

research

∙ 04/09/2016

Learning Compact Recurrent Neural Networks

Recurrent neural networks (RNNs), including long short-term memory (LSTM...

0 Zhiyun Lu, et al. ∙

research

∙ 10/06/2015

Structured Transforms for Small-Footprint Deep Learning

We consider the task of building compact deep learning pipelines suitabl...

0 Vikas Sindhwani, et al. ∙

research

∙ 12/29/2014

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels

We consider the problem of improving the efficiency of randomized Fourie...

0 Haim Avron, et al. ∙

research

∙ 08/09/2014

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...

0 Vikas Sindhwani, et al. ∙

research

∙ 12/27/2013

Near-separable Non-negative Matrix Factorization with ℓ_1- and Bregman Loss Functions

Recently, a family of tractable NMF algorithms have been proposed under ...

0 Abhishek Kumar, et al. ∙

research

∙ 10/03/2012

Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization

The separability assumption (Donoho & Stodden, 2003; Arora et al., 2012)...

0 Abhishek Kumar, et al. ∙

research

∙ 06/27/2012

Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization

We describe novel subgradient methods for a broad class of matrix optimi...

0 Haim Avron, et al. ∙

Vikas Sindhwani

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