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09/08/2023
Leveraging World Model Disentanglement in Value-Based Multi-Agent Reinforcement Learning
In this paper, we propose a novel model-based multi-agent reinforcement ...
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08/25/2023
Escaping the Sample Trap: Fast and Accurate Epistemic Uncertainty Estimation with Pairwise-Distance Estimators
This work introduces a novel approach for epistemic uncertainty estimati...
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06/04/2023
For SALE: State-Action Representation Learning for Deep Reinforcement Learning
In the field of reinforcement learning (RL), representation learning is ...
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05/09/2023
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
Reinforcement learning on high-dimensional and complex problems relies o...
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02/15/2023
ANSEL Photobot: A Robot Event Photographer with Semantic Intelligence
Our work examines the way in which large language models can be used for...
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02/02/2023
Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling
In this work, we demonstrate how to reliably estimate epistemic uncertai...
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11/28/2022
Hypernetworks for Zero-shot Transfer in Reinforcement Learning
In this paper, hypernetworks are trained to generate behaviors across a ...
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11/14/2022
NeurIPS 2022 Competition: Driving SMARTS
Driving SMARTS is a regular competition designed to tackle problems caus...
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10/03/2022
Uncertainty-Driven Active Vision for Implicit Scene Reconstruction
Multi-view implicit scene reconstruction methods have become increasingl...
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10/01/2022
Bayesian Q-learning With Imperfect Expert Demonstrations
Guided exploration with expert demonstrations improves data efficiency f...
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09/15/2022
Continuous MDP Homomorphisms and Homomorphic Policy Gradient
Abstraction has been widely studied as a way to improve the efficiency a...
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05/24/2022
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning
Continuous-time reinforcement learning offers an appealing formalism for...
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05/19/2022
IL-flOw: Imitation Learning from Observation using Normalizing Flows
We present an algorithm for Inverse Reinforcement Learning (IRL) from ex...
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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...
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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...
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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-...
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07/20/2021
Active 3D Shape Reconstruction from Vision and Touch
Humans build 3D understandings of the world through active object explor...
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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...
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01/12/2021
Learning Intuitive Physics with Multimodal Generative Models
Predicting the future interaction of objects when they come into contact...
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12/03/2020
Intervention Design for Effective Sim2Real Transfer
The goal of this work is to address the recent success of domain randomi...
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11/23/2020
Multimodal dynamics modeling for off-road autonomous vehicles
Dynamics modeling in outdoor and unstructured environments is difficult ...
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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...
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07/22/2020
Learning the Latent Space of Robot Dynamics for Cutting Interaction Inference
Utilization of latent space to capture a lower-dimensional representatio...
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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...
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07/07/2020
3D Shape Reconstruction from Vision and Touch
When a toddler is presented a new toy, their instinctual behaviour is to...
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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...
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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...
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12/02/2019
Detecting GAN generated errors
Despite an impressive performance from the latest GAN for generating hyp...
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10/23/2019
Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales
Neural Network based controllers hold enormous potential to learn comple...
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10/14/2019
Deep learning for Aerosol Forecasting
Reanalysis datasets combining numerical physics models and limited obser...
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10/05/2019
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning
Motivated by the recursive Newton-Euler formulation, we propose a novel ...
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06/02/2019
Learning Domain Randomization Distributions for Transfer of Locomotion Policies
Domain randomization (DR) is a successful technique for learning robust ...
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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...
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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...
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01/31/2019
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
Mesh models are a promising approach for encoding the structure of 3D ob...
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12/07/2018
Off-Policy Deep Reinforcement Learning without Exploration
Reinforcement learning traditionally considers the task of balancing exp...
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03/06/2018
Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning
We present an algorithm for rapidly learning controllers for robotics sy...
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02/27/2018
3D Object Super-Resolution
We consider the problem of scaling deep generative shape models to high-...
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09/21/2017
Cost Adaptation for Robust Decentralized Swarm Behaviour
The multi-agent swarm system is a robust paradigm which can drive effici...
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09/19/2017
Deep Reinforcement Learning that Matters
In recent years, significant progress has been made in solving challengi...
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08/14/2017
Benchmark Environments for Multitask Learning in Continuous Domains
As demand drives systems to generalize to various domains and problems, ...
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07/29/2017
Improved Adversarial Systems for 3D Object Generation and Reconstruction
This paper describes a new approach for training generative adversarial ...
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08/19/2009