CoRL 2018

75 papers

A Data-Efficient Approach to Precise and Controlled Pushing Maria Bauza, Francois R. Hogan, Alberto Rodriguez

A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning Nadia Figueroa, Aude Billard

Adaptable Replanning with Compressed Linear Action Models for Learning from Demonstrations Clement Gehring, Leslie Pack Kaelbling, Tomas Lozano-Perez

Automorphing Kernels for Nonstationarity in Mapping Unstructured Environments Ransalu Senanayake, Anthony Tompkins, Fabio Ramos

Batch Active Preference-Based Learning of Reward Functions Erdem Biyik, Dorsa Sadigh

Bayesian Generalized Kernel Inference for Terrain Traversability Mapping Tixiao Shan, Jinkun Wang, Brendan Englot, Kevin Doherty

Bayesian RL for Goal-Only Rewards Philippe Morere, Fabio Ramos

Benchmarking Reinforcement Learning Algorithms on Real-World Robots A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, James Bergstra

Benchmarks for Reinforcement Learning in Mixed-Autonomy Traffic Eugene Vinitsky, Aboudy Kreidieh, Luc Le Flem, Nishant Kheterpal, Kathy Jang, Cathy Wu, Fangyu Wu, Richard Liaw, Eric Liang, Alexandre M. Bayen

Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation Gregory Kahn, Adam Villaflor, Pieter Abbeel, Sergey Levine

Conditional Affordance Learning for Driving in Urban Environments Axel Sauer, Nikolay Savinov, Andreas Geiger

Curiosity Driven Exploration of Learned Disentangled Goal Spaces Adrien Laversanne-Finot, Alexandre Pere, Pierre-Yves Oudeyer

Deep Drone Racing: Learning Agile Flight in Dynamic Environments Elia Kaufmann, Antonio Loquercio, Rene Ranftl, Alexey Dosovitskiy, Vladlen Koltun, Davide Scaramuzza

Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects Jonathan Tremblay, Thang To, Balakumar Sundaralingam, Yu Xiang, Dieter Fox, Stan Birchfield

Dense Object Nets: Learning Dense Visual Object Descriptors by and for Robotic Manipulation Peter R. Florence, Lucas Manuelli, Russ Tedrake

Domain Randomization for Simulation-Based Policy Optimization with Transferability Assessment Fabio Muratore, Felix Treede, Michael Gienger, Jan Peters

Driving Policy Transfer via Modularity and Abstraction Matthias Mueller, Alexey Dosovitskiy, Bernard Ghanem, Vladlen Koltun

Dyadic Collaborative Manipulation Through Hybrid Trajectory Optimization Theodoros Stouraitis, Iordanis Chatzinikolaidis, Michael Gienger, Sethu Vijayakumar

Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics Ajinkya Jain, Scott Niekum

Energy-Based Hindsight Experience Prioritization Rui Zhao, Volker Tresp

ESIM: An Open Event Camera Simulator Henri Rebecq, Daniel Gehrig, Davide Scaramuzza

Expanding Motor Skills Using Relay Networks Visak CV Kumar, Sehoon Ha, C.Karen Liu

Fast 3D Modeling with Approximated Convolutional Kernels Vitor Guizilini, Fabio Ramos

Feature Learning for Scene Flow Estimation from LIDAR Arash K. Ushani, Ryan M. Eustice

Few-Shot Goal Inference for Visuomotor Learning and Planning Annie Xie, Avi Singh, Sergey Levine, Chelsea Finn

Global Search with Bernoulli Alternation Kernel for Task-Oriented Grasping Informed by Simulation Rika Antonova, Mia Kokic, Johannes A. Stork, Danica Kragic

GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning Jacky Liang, Viktor Makoviychuk, Ankur Handa, Nuttapong Chentanez, Miles Macklin, Dieter Fox

Grasp2Vec: Learning Object Representations from Self-Supervised Grasping Eric Jang, Coline Devin, Vincent Vanhoucke, Sergey Levine

Grounding Robot Plans from Natural Language Instructions with Incomplete World Knowledge Daniel Nyga, Subhro Roy, Rohan Paul, Daehyung Park, Mihai Pomarlan, Michael Beetz, Nicholas Roy

Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents Haonan Yu, Xiaochen Lian, Haichao Zhang, Wei Xu

HDNET: Exploiting HD Maps for 3D Object Detection Bin Yang, Ming Liang, Raquel Urtasun

HybridNet: Integrating Model-Based and Data-Driven Learning to Predict Evolution of Dynamical Systems Yun Long, Xueyuan She, Saibal Mukhopadhyay

Including Uncertainty When Learning from Human Corrections Dylan P. Losey, Marcia K. O’Malley

Inferring Geometric Constraints in Human Demonstrations Guru Subramani, Michael Zinn, Michael Gleicher

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

IntentNet: Learning to Predict Intention from Raw Sensor Data Sergio Casas, Wenjie Luo, Raquel Urtasun

Interpretable Latent Spaces for Learning from Demonstration Yordan Hristov, Alex Lascarides, Subramanian Ramamoorthy

Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation Fan Wang, Bo Zhou, Ke Chen, Tingxiang Fan, Xi Zhang, Jiangyong Li, Hao Tian, Jia Pan

Learning 6-DoF Grasping and Pick-Place Using Attention Focus Marcus Gualtieri, Robert Platt

Learning Audio Feedback for Estimating Amount and Flow of Granular Material Samuel Clarke, Travers Rhodes, Christopher G. Atkeson, Oliver Kroemer

Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal Jake Bruce, Niko Sunderhauf, Piotr Mirowski, Raia Hadsell, Michael Milford

Learning Neural Parsers with Deterministic Differentiable Imitation Learning Tanmay Shankar, Nicholas Rhinehart, Katharina Muelling, Kris M. Kitani

Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments Gregory J. Stein, Christopher Bradley, Nicholas Roy

Learning to Localize Using a LiDAR Intensity mAP Ioan Andrei Barsan, Shenlong Wang, Andrei Pokrovsky, Raquel Urtasun

Learning Under Misspecified Objective Spaces Andreea Bobu, Andrea Bajcsy, Jaime F. Fisac, Anca D. Dragan

Learning What Information to Give in Partially Observed Domains Rohan Chitnis, Leslie Pack Kaelbling, Tomas Lozano-Perez

Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization Paul-Edouard Sarlin, Frederic Debraine, Marcin Dymczyk, Roland Siegwart, Cesar Cadena

Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction Valts Blukis, Dipendra Misra, Ross A. Knepper, Yoav Artzi

Model-Based Reinforcement Learning via Meta-Policy Optimization Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel

Modular Meta-Learning Ferran Alet, Tomas Lozano-Perez, Leslie P. Kaelbling

Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs Patrick Wenzel, Qadeer Khan, Daniel Cremers, Laura Leal-Taixe

Motion Perception in Reinforcement Learning with Dynamic Objects Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun, Thomas Brox

Multi-Objective Model-Based Policy Search for Data-Efficient Learning with Sparse Rewards Rituraj Kaushik, Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret

Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation Pratyusha Sharma, Lekha Mohan, Lerrel Pinto, Abhinav Gupta

Neural Modular Control for Embodied Question Answering Abhishek Das, Georgia Gkioxari, Stefan Lee, Devi Parikh, Dhruv Batra

PAC-Bayes Control: Synthesizing Controllers That Provably Generalize to Novel Environments Anirudha Majumdar, Maxwell Goldstein

Particle Filter Networks with Application to Visual Localization Peter Karkus, David Hsu, Wee Sun Lee

Personalized Dynamics Models for Adaptive Assistive Navigation Systems Eshed OhnBar, Kris Kitani, Chieko Asakawa

Policies Modulating Trajectory Generators Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans, Vikas Sindhwani, Vincent Vanhoucke

Reinforcement Learning of Active Vision for Manipulating Objects Under Occlusions Ricson Cheng, Arpit Agarwal, Katerina Fragkiadaki

Reinforcement Learning of Phase Oscillators for Fast Adaptation to Moving Targets Guilherme Maeda, Okan Koc, Jun Morimoto

Reward Estimation for Variance Reduction in Deep Reinforcement Learning Joshua Romoff, Peter Henderson, Alexandre Piche, Vincent Francois-Lavet, Joelle Pineau

Risk-Aware Active Inverse Reinforcement Learning Daniel S. Brown, Yuchen Cui, Scott Niekum

ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning Through Imitation Ajay Mandlekar, Yuke Zhu, Animesh Garg, Jonathan Booher, Max Spero, Albert Tung, Julian Gao, John Emmons, Anchit Gupta, Emre Orbay, Silvio Savarese, Li Fei-Fei

Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning Frederik Ebert, Sudeep Dasari, Alex X. Lee, Sergey Levine, Chelsea Finn

Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, Sergey Levine

Sim-to-Real Reinforcement Learning for Deformable Object Manipulation Jan Matas, Stephen James, Andrew J. Davison

Sim-to-Real Transfer with Neural-Augmented Robot Simulation Florian Golemo, Adrien Ali Taiga, Aaron Courville, Pierre-Yves Oudeyer

Sparse Gaussian Process Temporal Difference Learning for Marine Robot Navigation John Martin, Jinkun Wang, Brendan Englot

SPNets: Differentiable Fluid Dynamics for Deep Neural Networks Connor Schenck, Dieter Fox

SURREAL: Open-Source Reinforcement Learning Framework and Robot Manipulation Benchmark Linxi Fan, Yuke Zhu, Jiren Zhu, Zihua Liu, Orien Zeng, Anchit Gupta, Joan Creus-Costa, Silvio Savarese, Li Fei-Fei

Task-Embedded Control Networks for Few-Shot Imitation Learning Stephen James, Michael Bloesch, Andrew J. Davison

The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems Spencer M. Richards, Felix Berkenkamp, Andreas Krause

Unpaired Learning of Dense Visual Depth Estimators for Urban Environments Vitor Guizilini, Fabio Ramos

Visual Curiosity: Learning to Ask Questions to Learn Visual Recognition Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, Devi Parikh