CoRL 2018

75 papers

Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning

Frederik Ebert, Sudeep Dasari, Alex X. Lee, Sergey Levine, Chelsea Finn

ESIM: an Open Event Camera Simulator

Henri Rebecq, Daniel Gehrig, Davide Scaramuzza

Interpretable Latent Spaces for Learning from Demonstration

Yordan Hristov, Alex Lascarides, Subramanian Ramamoorthy

IntentNet: Learning to Predict Intention from Raw Sensor Data

Sergio Casas, Wenjie Luo, Raquel Urtasun

A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning

Nadia Figueroa, Aude Billard

Policies Modulating Trajectory Generators

Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans

Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation

Pratyusha Sharma, Lekha Mohan, Lerrel Pinto, Abhinav Gupta

Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories

Yanfu Zhang, Wenshan Wang, Rogerio Bonatti, Daniel Maturana, Sebastian Scherer

ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation

Ajay Mandlekar, Yuke Zhu, Animesh Garg, Jonathan Booher, Max Spero

Dyadic collaborative Manipulation through Hybrid Trajectory Optimization

Theodoros Stouraitis, Iordanis Chatzinikolaidis, Michael Gienger, Sethu Vijayakumar

Modular meta-learning

Ferran Alet, Tomás Lozano-Pérez, Leslie P. Kaelbling

Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards

Rituraj Kaushik, Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret

Bayesian Generalized Kernel Inference for Terrain Traversability Mapping

Tixiao Shan, Jinkun Wang, Brendan Englot, Kevin Doherty

Sim-to-Real Transfer with Neural-Augmented Robot Simulation

Florian Golemo, Adrien Ali Taiga, Aaron Courville, Pierre-yves Oudeyer

Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation

Gregory Kahn, Adam Villaflor, Pieter Abbeel, Sergey Levine

Learning under Misspecified Objective Spaces

Andreea Bobu, Andrea Bajcsy, Jaime F. Fisac, Anca D. Dragan

Task-Embedded Control Networks for Few-Shot Imitation Learning

Stephen James, Michael Bloesch, Andrew J. Davison

SURREAL: Open-Source Reinforcement Learning Framework and Robot Manipulation Benchmark

Linxi Fan, Yuke Zhu, Jiren Zhu, Zihua Liu, Orien Zeng

Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics

Ajinkya Jain, Scott Niekum

Expanding Motor Skills using Relay Networks

Visak CV Kumar, Sehoon Ha, C. Karen Liu, C.Karen Liu

Sim-to-Real Reinforcement Learning for Deformable Object Manipulation

Jan Matas, Stephen James, Andrew J. Davison

Learning What Information to Give in Partially Observed Domains

Rohan Chitnis, Leslie Pack Kaelbling, Tomás Lozano-Pérez

Grounding Robot Plans from Natural Language Instructions with Incomplete World Knowledge

Daniel Nyga, Subhro Roy, Rohan Paul, Daehyung Park, Mihai Pomarlan

Domain Randomization for Simulation-Based Policy Optimization with Transferability Assessment

Fabio Muratore, Felix Treede, Michael Gienger, Jan Peters

Reward Estimation for Variance Reduction in Deep Reinforcement Learning

Joshua Romoff, Peter Henderson, Alexandre Piche, Vincent Francois-Lavet, Joelle Pineau

Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog

Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation

Rika Antonova, Mia Kokic, Johannes A. Stork, Danica Kragic

Reinforcement Learning of Phase Oscillators for Fast Adaptation to Moving Targets

Guilherme Maeda, Okan Koc, Jun Morimoto

Model-Based Reinforcement Learning via Meta-Policy Optimization

Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour

Learning to Localize Using a LiDAR Intensity Map

Ioan Andrei Barsan, Shenlong Wang, Andrei Pokrovsky, Raquel Urtasun

Learning Neural Parsers with Deterministic Differentiable Imitation Learning

Tanmay Shankar, Nicholas Rhinehart, Katharina Muelling, Kris M. Kitani

Benchmarking Reinforcement Learning Algorithms on Real-World Robots

A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, James Bergstra

HybridNet: Integrating Model-based and Data-driven Learning to Predict Evolution of Dynamical Systems

Yun Long, Xueyuan She, Saibal Mukhopadhyay

Learning Audio Feedback for Estimating Amount and Flow of Granular Material

Samuel Clarke, Travers Rhodes, Christopher G. Atkeson, Oliver Kroemer

Batch Active Preference-Based Learning of Reward Functions

Erdem Biyik, Dorsa Sadigh

Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction

Valts Blukis, Dipendra Misra, Ross A. Knepper, Yoav Artzi

Curiosity Driven Exploration of Learned Disentangled Goal Spaces

Adrien Laversanne-Finot, Alexandre Pere, Pierre-yves Oudeyer

Learning 6-DoF Grasping and Pick-Place Using Attention Focus

Marcus Gualtieri, Robert Platt

The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems

Spencer M. Richards, Felix Berkenkamp, Andreas Krause

Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization

Paul-Edouard Sarlin, Frederic Debraine, Marcin Dymczyk, Roland Siegwart, Cesar Cadena

Automorphing Kernels for Nonstationarity in Mapping Unstructured Environments

Ransalu Senanayake, Anthony Tompkins, Fabio Ramos

Adaptable replanning with compressed linear action models for learning from demonstrations

Clement Gehring, Leslie Pack Kaelbling, Tomás Lozano-Pérez

Reinforcement Learning of Active Vision for Manipulating Objects under Occlusions

Ricson Cheng, Arpit Agarwal, Katerina Fragkiadaki

Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation

Fan Wang, Bo Zhou, Ke Chen, Tingxiang Fan, Xi Zhang

Benchmarks for reinforcement learning in mixed-autonomy traffic

Eugene Vinitsky, Aboudy Kreidieh, Luc Le Flem, Nishant Kheterpal, Kathy Jang

Bayesian RL for Goal-Only Rewards

Philippe Morere, Fabio Ramos

Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation

Peter R. Florence, Lucas Manuelli, Russ Tedrake

Risk-Aware Active Inverse Reinforcement Learning

Daniel S. Brown, Yuchen Cui, Scott Niekum

Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal

Jake Bruce, Niko Sunderhauf, Piotr Mirowski, Raia Hadsell, Michael Milford

A Data-Efficient Approach to Precise and Controlled Pushing

Maria Bauza, Francois R. Hogan, Alberto Rodriguez

SPNets: Differentiable Fluid Dynamics for Deep Neural Networks

Connor Schenck, Dieter Fox

Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects

Jonathan Tremblay, Thang To, Balakumar Sundaralingam, Yu Xiang, Dieter Fox

PAC-Bayes Control: Synthesizing Controllers that Provably Generalize to Novel Environments

Anirudha Majumdar, Maxwell Goldstein

Feature Learning for Scene Flow Estimation from LIDAR

Arash K. Ushani, Ryan M. Eustice

GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning

Jacky Liang, Viktor Makoviychuk, Ankur Handa, Nuttapong Chentanez, Miles Macklin

Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs

Patrick Wenzel, Qadeer Khan, Daniel Cremers, Laura Leal-Taixé

Conditional Affordance Learning for Driving in Urban Environments

Axel Sauer, Nikolay Savinov, Andreas Geiger

Inferring geometric constraints in human demonstrations

Guru Subramani, Michael Zinn, Michael Gleicher

Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments

Gregory J. Stein, Christopher Bradley, Nicholas Roy

Unpaired Learning of Dense Visual Depth Estimators for Urban Environments

Vitor Guizilini, Fabio Ramos

Fast 3D Modeling with Approximated Convolutional Kernels

Vitor Guizilini, Fabio Ramos

Sparse Gaussian Process Temporal Difference Learning for Marine Robot Navigation

John Martin, Jinkun Wang, Brendan Englot

Particle Filter Networks with Application to Visual Localization

Peter Karkus, David Hsu, Wee Sun Lee

Motion Perception in Reinforcement Learning with Dynamic Objects

Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun, Thomas Brox

HDNET: Exploiting HD Maps for 3D Object Detection

Bin Yang, Ming Liang, Raquel Urtasun

Deep Drone Racing: Learning Agile Flight in Dynamic Environments

Elia Kaufmann, Antonio Loquercio, Rene Ranftl, Alexey Dosovitskiy, Vladlen Koltun

Including Uncertainty when Learning from Human Corrections

Dylan P. Losey, Marcia K. O’Malley

Energy-Based Hindsight Experience Prioritization

Rui Zhao, Volker Tresp

Grasp2Vec: Learning Object Representations from Self-Supervised Grasping

Eric Jang, Coline Devin, Vincent Vanhoucke, Sergey Levine

Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents

Haonan Yu, Xiaochen Lian, Haichao Zhang, Wei Xu

Visual Curiosity: Learning to Ask Questions to Learn Visual Recognition

Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, Devi Parikh

Neural Modular Control for Embodied Question Answering

Abhishek Das, Georgia Gkioxari, Stefan Lee, Devi Parikh, Dhruv Batra

Few-Shot Goal Inference for Visuomotor Learning and Planning

Annie Xie, Avi Singh, Sergey Levine, Chelsea Finn

Personalized Dynamics Models for Adaptive Assistive Navigation Systems

Kris Kitani, Chieko Asakawa, Eshed Ohn-Bar, Eshed OhnBar

Driving Policy Transfer via Modularity and Abstraction

Matthias Mueller, Alexey Dosovitskiy, Bernard Ghanem, Vladlen Koltun