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09/11/2023
Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models
A crucial design decision for any robot learning pipeline is the choice ...
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09/06/2023
Robotic Table Tennis: A Case Study into a High Speed Learning System
We present a deep-dive into a real-world robotic learning system that, i...
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06/14/2023
Agile Catching with Whole-Body MPC and Blackbox Policy Learning
We address a benchmark task in agile robotics: catching objects thrown a...
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05/24/2023
Barkour: Benchmarking Animal-level Agility with Quadruped Robots
Animals have evolved various agile locomotion strategies, such as sprint...
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05/20/2023
Safely Learning Dynamical Systems
A fundamental challenge in learning an unknown dynamical system is to re...
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02/02/2023
Mnemosyne: Learning to Train Transformers with Transformers
Training complex machine learning (ML) architectures requires a compute ...
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12/13/2022
Single-Level Differentiable Contact Simulation
We present a differentiable formulation of rigid-body contact dynamics f...
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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...
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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...
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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...
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08/02/2022
Implicit Two-Tower Policies
We present a new class of structured reinforcement learning policy-archi...
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04/01/2022
Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language
Large foundation models can exhibit unique capabilities depending on the...
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03/16/2022
Multiscale Sensor Fusion and Continuous Control with Neural CDEs
Though robot learning is often formulated in terms of discrete-time Mark...
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10/08/2021
Hybrid Random Features
We propose a new class of random feature methods for linearizing softmax...
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09/10/2021
Trajectory Optimization with Optimization-Based Dynamics
We present a framework for bi-level trajectory optimization in which a s...
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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,...
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11/24/2020
Safely Learning Dynamical Systems from Short Trajectories
A fundamental challenge in learning to control an unknown dynamical syst...
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10/27/2020
Transporter Networks: Rearranging the Visual World for Robotic Manipulation
Robotic manipulation can be formulated as inducing a sequence of spatial...
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10/16/2020
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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08/13/2020
Learning Stability Certificates from Data
Many existing tools in nonlinear control theory for establishing stabili...
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06/19/2020
An Ode to an ODE
We present a new paradigm for Neural ODE algorithms, calledODEtoODE, whe...
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05/05/2020
Time Dependence in Non-Autonomous Neural ODEs
Neural Ordinary Differential Equations (ODEs) are elegant reinterpretati...
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03/31/2020
Robotic Table Tennis with Model-Free Reinforcement Learning
We propose a model-free algorithm for learning efficient policies capabl...
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03/30/2020
Stochastic Flows and Geometric Optimization on the Orthogonal Group
We present a new class of stochastic, geometrically-driven optimization ...
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10/07/2019
Policies Modulating Trajectory Generators
We propose an architecture for learning complex controllable behaviors b...
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07/29/2019
Learning Stabilizable Nonlinear Dynamics with Contraction-Based Regularization
We propose a novel framework for learning stabilizable nonlinear dynamic...
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07/08/2019
Data Efficient Reinforcement Learning for Legged Robots
We present a model-based framework for robot locomotion that achieves wa...
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05/23/2019
Teleoperator Imitation with Continuous-time Safety
Learning to effectively imitate human teleoperators, with generalization...
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03/07/2019
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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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...
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07/31/2018
Learning Stabilizable Dynamical Systems via Control Contraction Metrics
We propose a novel framework for learning stabilizable nonlinear dynamic...
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05/20/2018
Optimizing Simulations with Noise-Tolerant Structured Exploration
We propose a simple drop-in noise-tolerant replacement for the standard ...
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04/13/2018
Learning Contracting Vector Fields For Stable Imitation Learning
We propose a new non-parametric framework for learning incrementally sta...
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04/06/2018
Structured Evolution with Compact Architectures for Scalable Policy Optimization
We present a new method of blackbox optimization via gradient approximat...
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10/15/2017
Manifold Regularization for Kernelized LSTD
Policy evaluation or value function or Q-function approximation is a key...
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11/22/2016
Geometry of 3D Environments and Sum of Squares Polynomials
Motivated by applications in robotics and computer vision, we study prob...
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08/02/2016
Hierarchically Compositional Kernels for Scalable Nonparametric Learning
We propose a novel class of kernels to alleviate the high computational ...
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05/29/2016
Recycling Randomness with Structure for Sublinear time Kernel Expansions
We propose a scheme for recycling Gaussian random vectors into structure...
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04/09/2016
Learning Compact Recurrent Neural Networks
Recurrent neural networks (RNNs), including long short-term memory (LSTM...
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10/06/2015
Structured Transforms for Small-Footprint Deep Learning
We consider the task of building compact deep learning pipelines suitabl...
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12/29/2014
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
We consider the problem of improving the efficiency of randomized Fourie...
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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...
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12/27/2013
Near-separable Non-negative Matrix Factorization with â„“_1- and Bregman Loss Functions
Recently, a family of tractable NMF algorithms have been proposed under ...
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10/03/2012
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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06/27/2012
