CoRL

Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics

Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier, Thomas Lampe, Jost Tobias Springenberg, Roland Hafner, Francesco Romano, Jonas Buchli, Nicolas Heess, Martin Riedmiller

Comments Presented at the 3rd Conference on Robot Learning (CoRL 2019), Osaka, Japan. Video: https://youtu.be/eUqQDLQXb7I

RoboNet: Large-Scale Multi-Robot Learning

Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, Chelsea Finn

Comments accepted at the Conference on Robot Learning (CoRL) 2019

Quasi-Newton Trust Region Policy Optimization

Devesh Jha, Arvind Raghunathan, Diego Romeres

Comments 3rd Conference on Robot Learning (CoRL 2019)

Learning to Navigate Using Mid-Level Visual Priors

Alexander Sax, Jeffrey O. Zhang, Bradley Emi, Amir Zamir, Silvio Savarese, Leonidas Guibas, Jitendra Malik

Comments In Conference on Robot Learning, 2019. See project website and demos at http://perceptual.actor/

Learning Latent Plans from Play

Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, Pierre Sermanet

Comments Published at CoRL 2019 (3rd Conference on Robot Learning, Osaka, Japan)

To Follow or not to Follow: Selective Imitation Learning from Observations

Youngwoon Lee, Edward S. Hu, Zhengyu Yang, Joseph J. Lim

Comments Published at the Conference on Robot Learning (CoRL) 2019

ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots

Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte, Abhishek Gupta, Sergey Levine, Vikash Kumar

Comments Published @ CoRL2019. For details visit - http://www.roboticsbenchmarks.org

Journal ref Conference on Robot Learning, 2019

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

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

Comments Conference on Robot Learning (CoRL) 2019

Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances

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

Comments Conference on Robot Learning (CoRL 2019)

Two Stream Networks for Self-Supervised Ego-Motion Estimation

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

Comments Conference on Robot Learning (CoRL 2019)

Experience-Embedded Visual Foresight

Lin Yen-Chen, Maria Bauza, Phillip Isola

Comments CoRL 2019. Project website: http://yenchenlin.me/evf/

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

Kevin Sebastian Luck, Heni Ben Amor, Roberto Calandra

Comments Accepted for the Conference on Robot Learning 2019

MAME : Model-Agnostic Meta-Exploration

Swaminathan Gurumurthy, Sumit Kumar, Katia Sycara

Comments CoRL 2019

Worst Cases Policy Gradients

Yichuan Charlie Tang, Jian Zhang, Ruslan Salakhutdinov

Comments Conference on Robot Learning 2019 (CoRL 2019)

Nonverbal Robot Feedback for Human Teachers

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

Comments CoRL 2019

A Divergence Minimization Perspective on Imitation Learning Methods

Seyed Kamyar Seyed Ghasemipour, Richard Zemel, Shixiang Gu

Comments Published at Conference on Robot Learning (CoRL) 2019. For datasets and reproducing results please refer to https://github.com/KamyarGh/rl_swiss/blob/master/reproducing/fmax_paper.md

S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes

Yuzhe Qin, Rui Chen, Hao Zhu, Meng Song, Jing Xu, Hao Su

Comments Accepted at the Conference on Robot Learning (CoRL) 2019. Project webpage is available here: https://sites.google.com/view/s4ggrapsing

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

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

Comments Conference on Robot Learning (CoRL), 2019

Asynchronous Methods for Model-Based Reinforcement Learning

Yunzhi Zhang, Ignasi Clavera, Boren Tsai, Pieter Abbeel

Comments 10 pages, CoRL 2019

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

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

Comments Published at CoRL 2019

Contextual Imagined Goals for Self-Supervised Robotic Learning

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

Comments 12 pages, to be presented at Conference on Robot Learning (CoRL) 2019. Project website: https://ccrig.github.io/

HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators

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

Comments Conference on Robot Learning (CoRL) 2019

Identifying Unknown Instances for Autonomous Driving

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

Comments 3rd Conference on Robot Learning (CoRL 2019)

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

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

Comments To appear at 2019 Conference on Robot Learning (CoRL)

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

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

Comments Conference on Robot Learning (CoRL) 2019

Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction

Ajay Jain, Sergio Casas, Renjie Liao, Yuwen Xiong, Song Feng, Sean Segal, Raquel Urtasun

Comments CoRL 2019

Conditional Driving from Natural Language Instructions

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

Comments Accepted by the 3rd Conference on Robot Learning, Osaka, Japan (CoRL 2019)

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

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

Comments Accepted at CoRL 2019

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

Eunah Jung, Nan Yang, Daniel Cremers

Comments Accepted by the 3rd Conference on Robot Learning, Osaka, Japan (CoRL 2019). The first two authors contributed equally to this paper

Regularizing Model-Based Planning with Energy-Based Models

Rinu Boney, Juho Kannala, Alexander Ilin

Comments Conference on Robot Learning 2019