CoRL 2019

110 papers

Learning Reactive Motion Policies in Multiple Task Spaces from Human Demonstrations

M. Asif Rana, Anqi Li, Harish Ravichandar, Mustafa Mukadam, Sonia Chernova

Entity Abstraction in Visual Model-Based Reinforcement Learning

Rishi Veerapaneni, John D. Co-Reyes, Michael Chang, Michael Janner, Chelsea Finn

Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated Flight

Valts Blukis, Yannick Terme, Eyvind Niklasson, Ross A. Knepper, Yoav Artzi

Kernel Trajectory Maps for Multi-Modal Probabilistic Motion Prediction

Weiming Zhi, Lionel Ott, Fabio Ramos

Towards Learning to Detect and Predict Contact Events on Vision-based Tactile Sensors

Yazhan Zhang, Weihao Yuan, Zicheng Kan, Michael Yu Wang

Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods

Ching-An Cheng, Xinyan Yan, Byron Boots

The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentation

Christopher Xie, Yu Xiang, Arsalan Mousavian, Dieter Fox

An Online Learning Procedure for Feedback Linearization Control without Torque Measurements

M. Capotondi, G. Turrisi, C. Gaz, V. Modugno, G. Oriolo

PyRoboLearn: A Python Framework for Robot Learning Practitioners

Brian Delhaisse, Leonel Rozo, Darwin G. Caldwell

Asynchronous Methods for Model-Based Reinforcement Learning

Yunzhi Zhang, Ignasi Clavera, Boren Tsai, Pieter Abbeel

Certified Adversarial Robustness for Deep Reinforcement Learning

Björn Lütjens, Michael Everett, Jonathan P How, Jonathan P. How

Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments

Martin Weiss, Simon Chamorro, Roger Girgis, Margaux Luck, Samira E. Kahou

ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots

Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte, Abhishek Gupta

Learning to Generalize Kinematic Models to Novel Objects

Ben Abbatematteo, Stefanie Tellex, George Konidaris

Receding Horizon Curiosity

Matthias Schultheis, Boris Belousov, Hany Abdulsamad, Jan Peters

A Divergence Minimization Perspective on Imitation Learning Methods

Seyed Kamyar Seyed Ghasemipour, Richard Zemel, Shixiang Gu

Understanding Teacher Gaze Patterns for Robot Learning

Akanksha Saran, Elaine Schaertl Short, Andrea Thomaz, Scott Niekum

Variational Optimization Based Reinforcement Learning for Infinite Dimensional Stochastic Systems

Ethan N. Evans, Marcus A. Periera, George I. Boutselis, Evangelos A. Theodorou

Learning Parametric Constraints in High Dimensions from Demonstrations

Glen Chou, Necmiye Ozay, Dmitry Berenson

Language-guided Semantic Mapping and Mobile Manipulation in Partially Observable Environments

Siddharth Patki, Ethan Fahnestock, Thomas M. Howard, Matthew R. Walter

Dynamic Experience Replay

Jieliang Luo, Hui Li

Asking Easy Questions: A User-Friendly Approach to Active Reward Learning

Erdem B\iy\ik, Malayandi Palan, Nicholas C. Landolfi, Dylan P. Losey, Dorsa Sadigh

Active Domain Randomization

Bhairav Mehta, Manfred Diaz, Florian Golemo, Christopher J. Pal, Liam Paull

Macro-Action-Based Deep Multi-Agent Reinforcement Learning

Yuchen Xiao, Joshua Hoffman, Christopher Amato

Scene-level Pose Estimation for Multiple Instances of Densely Packed Objects

Chaitanya Mitash, Bowen Wen, Kostas Bekris, Abdeslam Boularias

Learning Latent Plans from Play

Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson

Deep Dynamics Models for Learning Dexterous Manipulation

Anusha Nagabandi, Kurt Konolige, Sergey Levine, Vikash Kumar

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman

Worst Cases Policy Gradients

Yichuan Charlie Tang, Jian Zhang, Ruslan Salakhutdinov

Model-based Behavioral Cloning with Future Image Similarity Learning

Alan Wu, AJ Piergiovanni, Michael S. Ryoo

Two Stream Networks for Self-Supervised Ego-Motion Estimation

Rares Ambrus, Vitor Guizilini, Jie Li, Sudeep Pillai Adrien Gaidon

Nonverbal Robot Feedback for Human Teachers

Sandy H. Huang, Isabella Huang, Ravi Pandya, Anca D. Dragan

Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning

Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, Karol Hausman

Experience-Embedded Visual Foresight

Lin Yen-Chen, Maria Bauza, Phillip Isola

Inferring Task Goals and Constraints using Bayesian Nonparametric Inverse Reinforcement Learning

Daehyung Park, Michael Noseworthy, Rohan Paul, Subhro Roy, Nicholas Roy

Deep Value Model Predictive Control

David Hoeller, Farbod Farshidian, Marco Hutter

Graph-Structured Visual Imitation

Maximilian Sieb, Zhou Xian, Audrey Huang, Oliver Kroemer, Katerina Fragkiadaki

Locally Weighted Regression Pseudo-Rehearsal for Adaptive Model Predictive Control

Grady R. Williams, Brian Goldfain, Keuntaek Lee, Jason Gibson, James M. Rehg

Learning value functions with relational state representations for guiding task-and-motion planning

Beomjoon Kim, Luke Shimanuki

Quasi-Newton Trust Region Policy Optimization

Devesh K. Jha, Arvind U. Raghunathan, Diego Romeres

Task-Conditioned Variational Autoencoders for Learning Movement Primitives

Michael Noseworthy, Rohan Paul, Subhro Roy, Daehyung Park, Nicholas Roy

End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds

Yin Zhou, Pei Sun, Yu Zhang, Dragomir Anguelov, Jiyang Gao

MAME : Model-Agnostic Meta-Exploration

Swaminathan Gurumurthy, Sumit Kumar, Katia Sycara

Counter-example Guided Learning of Bounds on Environment Behavior

Yuxiao Chen, Sumanth Dathathri, Tung Phan-Minh, Richard M. Murray

RoboNet: Large-Scale Multi-Robot Learning

Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher

Disentangled Relational Representations for Explaining and Learning from Demonstration

Yordan Hristov, Daniel Angelov, Michael Burke, Alex Lascarides, Subramanian Ramamoorthy

Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning

Kevin Sebastian Luck, Heni Ben Amor, Roberto Calandra

Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments

Rémy Portelas, Cédric Colas, Katja Hofmann, Pierre-yves Oudeyer

Graph Policy Gradients for Large Scale Robot Control

Arbaaz Khan, Ekaterina Tolstaya, Alejandro Ribeiro, Vijay Kumar

Learning Compact Models for Planning with Exogenous Processes

Rohan Chitnis, Tomás Lozano-Pérez

Learning to Navigate Using Mid-Level Visual Priors

Alexander Sax, Jeffrey O. Zhang, Bradley Emi, Amir Zamir, Silvio Savarese

Multi-Agent Reinforcement Learning with Multi-Step Generative Models

Orr Krupnik, Igor Mordatch, Aviv Tamar

Energy-efficient Path Planning for Ground Robots by and Combining Air and Ground Measurements

Minghan Wei, Volkan Isler

Learning from My Partner’s Actions: Roles in Decentralized Robot Teams

Dylan P. Losey, Mengxi Li, Jeannette Bohg, Dorsa Sadigh

Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics

Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier, Thomas Lampe, Tobias Springenberg

AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers

Andrey Kurenkov, Ajay Mandlekar, Roberto Martín-Martín, Silvio Savarese, Animesh Garg

Stochastic Optimal Control as Approximate Input Inference

Joe Watson, Hany Abdulsamad, Jan Peters

Provably Robust Blackbox Optimization for Reinforcement Learning

Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Deepali Jain

Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks

Ekaterina Tolstaya, Fernando Gama, James Paulos, George Pappas, Vijay Kumar

Connectivity Guaranteed Multi-robot Navigation via Deep Reinforcement Learning

Juntong Lin, Xuyun Yang, Peiwei Zheng, Hui Cheng

Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light

Eunah Jung, Nan Yang, Daniel Cremers

HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints

Michael Lutter, Boris Belousov, Kim Listmann, Debora Clever, Jan Peters

A Learnable Safety Measure

Steve Heim, Alexander von Rohr, Sebastian Trimpe, Alexander Badri-Spröwitz

Learning Navigation Subroutines from Egocentric Videos

Ashish Kumar, Saurabh Gupta, Jitendra Malik

HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators

Chengshu Li, Fei Xia, Roberto Martín-Martín, Silvio Savarese

PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning

Iou-Jen Liu, Raymond A. Yeh, Alexander G. Schwing

Imagined Value Gradients: Model-Based Policy Optimization with Tranferable Latent Dynamics Models

Arunkumar Byravan, Jost Tobias Springenberg, Abbas Abdolmaleki, Roland Hafner, Michael Neunert

Adversarial Active Exploration for Inverse Dynamics Model Learning

Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann, Chun-Yi Lee

Conditional Driving from Natural Language Instructions

Junha Roh, Chris Paxton, Andrzej Pronobis, Ali Farhadi, Dieter Fox

Contextual Imagined Goals for Self-Supervised Robotic Learning

Ashvin Nair, Shikhar Bahl, Alexander Khazatsky, Vitchyr Pong, Glen Berseth

Self-Paced Contextual Reinforcement Learning

Pascal Klink, Hany Abdulsamad, Boris Belousov, Jan Peters

Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances

Vitor Guizilini, Jie Li, Rares Ambrus, Sudeep Pillai, Adrien Gaidon

Learning to Manipulate Object Collections Using Grounded State Representations

Matthew Wilson, Tucker Hermans

A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots

Nicolai A. Lynnerup, Laura Nolling, Rasmus Hasle, John Hallam

Bayesian Optimization in Variational Latent Spaces with Dynamic Compression

Rika Antonova, Akshara Rai, Tianyu Li, Danica Kragic

TuneNet: One-Shot Residual Tuning for System Identification and Sim-to-Real Robot Task Transfer

Adam Allevato, Elaine Schaertl Short, Mitch Pryor, Andrea Thomaz

Leveraging exploration in off-policy algorithms via normalizing flows

Bogdan Mazoure, Thang Doan, Audrey Durand, Joelle Pineau, R Devon Hjelm

Combining Optimal Control and Learning for Visual Navigation in Novel Environments

Somil Bansal, Varun Tolani, Saurabh Gupta, Jitendra Malik, Claire Tomlin

Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction

Ajay Jain, Sergio Casas, Renjie Liao, Yuwen Xiong, Song Feng

Vision-and-Dialog Navigation

Jesse Thomason, Michael Murray, Maya Cakmak, Luke Zettlemoyer

Identifying Unknown Instances for Autonomous Driving

Kelvin Wong, Shenlong Wang, Mengye Ren, Ming Liang, Raquel Urtasun

Model-Based Planning with Energy-Based Models

Yilun Du, Toru Lin, Igor Mordatch

Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning

Felix Leibfried, Jordi Grau-moya

Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations

Daniel S. Brown, Wonjoon Goo, Scott Niekum

Learning Locomotion Skills for Cassie: Iterative Design and Sim-to-Real

Zhaoming Xie, Patrick Clary, Jeremy Dao, Pedro Morais, Jonanthan Hurst

Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information

Dan Barnes, Rob Weston, Ingmar Posner

A correct formulation for the Orientation Dynamic Movement Primitives for robot control in the Cartesian space

Leonidas Koutras, Zoe Doulgeri

Predictive Safety Network for Resource-constrained Multi-agent Systems

Meng Guo, Mathias Bürger

Optimizing Sequences of Probabilistic Manipulation Skills Learned from Demonstration

Lukas Schwenkel, Meng Guo, Mathias Bürger

Variational Inference MPC for Bayesian Model-based Reinforcement Learning

Masashi Okada, Tadahiro Taniguchi

Learning from demonstration with model-based Gaussian process

Noémie Jaquier, David Ginsbourger, Sylvain Calinon

Bayesian Optimization Meets Riemannian Manifolds in Robot Learning

Noémie Jaquier, Leonel Rozo, Sylvain Calinon, Mathias Bürger

Perceptual Attention-based Predictive Control

Keuntaek Lee, Gabriel Nakajima An, Viacheslav Zakharov, Evangelos A. Theodorou

Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping

Mustafa Mukadam, Ching-An Cheng, Dieter Fox, Byron Boots, Nathan Ratliff

Semi-Supervised Learning of Decision-Making Models for Human-Robot Collaboration

Vaibhav V. Unhelkar, Shen Li, Julie A Shah, Julie A. Shah

Regularizing Model-Based Planning with Energy-Based Models

Rinu Boney, Juho Kannala, Alexander Ilin

Hybrid system identification using switching density networks

Michael Burke, Yordan Hristov, Subramanian Ramamoorthy

Curious iLQR: Resolving Uncertainty in Model-based RL

Sarah Bechtle, Yixin Lin, Akshara Rai, Ludovic Righetti, Franziska Meier

MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning

Bohan Wu, Iretiayo Akinola, Jacob Varley, Peter K. Allen

Combining Deep Learning and Verification for Precise Object Instance Detection

Siddharth Ancha, Junyu Nan, David Held