b'David Meger'

research

∙ 02/02/2023

Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling

In this work, we demonstrate how to reliably estimate epistemic uncertai...

Lucas Berry, et al. ∙

research

∙ 11/28/2022

Hypernetworks for Zero-shot Transfer in Reinforcement Learning

In this paper, hypernetworks are trained to generate behaviors across a ...

Sahand Rezaei-Shoshtari, et al. ∙

research

∙ 11/14/2022

NeurIPS 2022 Competition: Driving SMARTS

Driving SMARTS is a regular competition designed to tackle problems caus...

Amir Rasouli, et al. ∙

research

∙ 10/03/2022

Uncertainty-Driven Active Vision for Implicit Scene Reconstruction

Multi-view implicit scene reconstruction methods have become increasingl...

Edward J. Smith, et al. ∙

research

∙ 10/01/2022

Bayesian Q-learning With Imperfect Expert Demonstrations

Guided exploration with expert demonstrations improves data efficiency f...

Fengdi Che, et al. ∙

research

∙ 09/15/2022

Continuous MDP Homomorphisms and Homomorphic Policy Gradient

Abstraction has been widely studied as a way to improve the efficiency a...

Sahand Rezaei-Shoshtari, et al. ∙

research

∙ 05/24/2022

Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning

Continuous-time reinforcement learning offers an appealing formalism for...

Harley Wiltzer, et al. ∙

research

∙ 05/19/2022

IL-flOw: Imitation Learning from Observation using Normalizing Flows

We present an algorithm for Inverse Reinforcement Learning (IRL) from ex...

Wei-Di Chang, et al. ∙

research

∙ 01/28/2022

Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error

In this work, we study the use of the Bellman equation as a surrogate ob...

Scott Fujimoto, et al. ∙

research

∙ 12/09/2021

Trajectory-Constrained Deep Latent Visual Attention for Improved Local Planning in Presence of Heterogeneous Terrain

We present a reward-predictive, model-based deep learning method featuri...

Stefan Wapnick, et al. ∙

research

∙ 10/27/2021

An Autonomous Probing System for Collecting Measurements at Depth from Small Surface Vehicles

This paper presents the portable autonomous probing system (APS), a low-...

Yuying Huang, et al. ∙

research

∙ 07/20/2021

Active 3D Shape Reconstruction from Vision and Touch

Humans build 3D understandings of the world through active object explor...

Edward J. Smith, et al. ∙

research

∙ 06/12/2021

A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation

Marginalized importance sampling (MIS), which measures the density ratio...

Scott Fujimoto, et al. ∙

research

∙ 01/12/2021

Learning Intuitive Physics with Multimodal Generative Models

Predicting the future interaction of objects when they come into contact...

Sahand Rezaei-Shoshtari, et al. ∙

research

∙ 12/03/2020

Intervention Design for Effective Sim2Real Transfer

The goal of this work is to address the recent success of domain randomi...

Melissa Mozifian, et al. ∙

research

∙ 11/23/2020

Multimodal dynamics modeling for off-road autonomous vehicles

Dynamics modeling in outdoor and unstructured environments is difficult ...

Jean-François Tremblay, et al. ∙

research

∙ 11/18/2020

Seeing Through your Skin: Recognizing Objects with a Novel Visuotactile Sensor

We introduce a new class of vision-based sensor and associated algorithm...

Francois Robert Hogan, et al. ∙

research

∙ 07/22/2020

Learning the Latent Space of Robot Dynamics for Cutting Interaction Inference

Utilization of latent space to capture a lower-dimensional representatio...

Sahand Rezaei-Shoshtari, et al. ∙

research

∙ 07/12/2020

An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay

Prioritized Experience Replay (PER) is a deep reinforcement learning tec...

Scott Fujimoto, et al. ∙

research

∙ 07/07/2020

3D Shape Reconstruction from Vision and Touch

When a toddler is presented a new toy, their instinctual behaviour is to...

Edward J. Smith, et al. ∙

research

∙ 06/29/2020

Vision-Based Goal-Conditioned Policies for Underwater Navigation in the Presence of Obstacles

We present Nav2Goal, a data-efficient and end-to-end learning method for...

Travis Manderson, et al. ∙

research

∙ 04/09/2020

Learning to Drive Off Road on Smooth Terrain in Unstructured Environments Using an On-Board Camera and Sparse Aerial Images

We present a method for learning to drive on smooth terrain while simult...

Travis Manderson, et al. ∙

research

∙ 12/02/2019

Detecting GAN generated errors

Despite an impressive performance from the latest GAN for generating hyp...

Xiru Zhu, et al. ∙

research

∙ 10/23/2019

Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales

Neural Network based controllers hold enormous potential to learn comple...

Sanjay Thakur, et al. ∙

research

∙ 10/14/2019

Deep learning for Aerosol Forecasting

Reanalysis datasets combining numerical physics models and limited obser...

Caleb Hoyne, et al. ∙

research

∙ 10/05/2019

Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning

Motivated by the recursive Newton-Euler formulation, we propose a novel ...

Sahand Rezaei-Shoshtari, et al. ∙

research

∙ 06/02/2019

Learning Domain Randomization Distributions for Transfer of Locomotion Policies

Domain randomization (DR) is a successful technique for learning robust ...

Melissa Mozifian, et al. ∙

research

∙ 04/18/2019

Human Motion Prediction via Pattern Completion in Latent Representation Space

Inspired by ideas in cognitive science, we propose a novel and general a...

Yi Tian Xu, et al. ∙

research

∙ 03/13/2019

Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks

Diversity of environments is a key challenge that causes learned robotic...

Sanjay Thakur, et al. ∙

research

∙ 01/31/2019

GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects

Mesh models are a promising approach for encoding the structure of 3D ob...

Edward J. Smith, et al. ∙

research

∙ 12/07/2018

Off-Policy Deep Reinforcement Learning without Exploration

Reinforcement learning traditionally considers the task of balancing exp...

Scott Fujimoto, et al. ∙

research

∙ 03/06/2018

Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning

We present an algorithm for rapidly learning controllers for robotics sy...

Juan Camilo Gamboa Higuera, et al. ∙

research

∙ 02/27/2018

3D Object Super-Resolution

We consider the problem of scaling deep generative shape models to high-...

Edward Smith, et al. ∙

research

∙ 09/21/2017

Cost Adaptation for Robust Decentralized Swarm Behaviour

The multi-agent swarm system is a robust paradigm which can drive effici...

Peter Henderson, et al. ∙

research

∙ 09/19/2017

Deep Reinforcement Learning that Matters

In recent years, significant progress has been made in solving challengi...

Peter Henderson, et al. ∙

research

∙ 08/14/2017

Benchmark Environments for Multitask Learning in Continuous Domains

As demand drives systems to generalize to various domains and problems, ...

Peter Henderson, et al. ∙

research

∙ 07/29/2017

Improved Adversarial Systems for 3D Object Generation and Reconstruction

This paper describes a new approach for training generative adversarial ...

Edward Smith, et al. ∙

research

∙ 08/19/2009

Semantic Robot Vision Challenge: Current State and Future Directions

The Semantic Robot Vision Competition provided an excellent opportunity ...

Scott Helmer, et al. ∙

David Meger

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