CoRL

MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Yuning Chai, Benjamin Sapp, Mayank Bansal, Dragomir Anguelov

Comments Appears in CoRL 2019

Vision-and-Dialog Navigation

Jesse Thomason, Michael Murray, Maya Cakmak, Luke Zettlemoyer

Comments Conference on Robot Learning (CoRL) 2019

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

Daniel S. Brown, Wonjoon Goo, Scott Niekum

Comments In proceedings of 3rd Conference on Robot Learning (CoRL) 2019

Learning from demonstration with model-based Gaussian process

Noémie Jaquier, David Ginsbourger, Sylvain Calinon

Comments Accepted in CoRL'19, 11 pages, 7 figures, 1 table, 2 appendices

Bayesian Optimization Meets Riemannian Manifolds in Robot Learning

Noémie Jaquier, Leonel Rozo, Sylvain Calinon, Mathias Bürger

Comments Accepted in CoRL'19, 14 pages, 9 figures, 5 tables, 7 appendices

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

Chaitanya Mitash, Bowen Wen, Kostas Bekris, Abdeslam Boularias

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

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

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

Comments Conference on Robot Learning (CoRL) 2019

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

Erdem Bıyık, Malayandi Palan, Nicholas C. Landolfi, Dylan P. Losey, Dorsa Sadigh

Comments Proceedings of the 3rd Conference on Robot Learning (CoRL), October 2019

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

Bohan Wu, Iretiayo Akinola, Jacob Varley, Peter Allen

Comments Accepted at 3rd Conference on Robot Learning (CoRL 2019). Oral Presentation

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

Arunkumar Byravan, Jost Tobias Springenberg, Abbas Abdolmaleki, Roland Hafner, Michael Neunert, Thomas Lampe, Noah Siegel, Nicolas Heess, Martin Riedmiller

Comments To appear at the 3rd annual Conference on Robot Learning, Osaka, Japan (CoRL 2019). 24 pages including appendix (main paper - 8 pages)

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

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

Comments 10 pages, 7 figures, Accepted to conference on Robot Learning (CoRL 2019)

Receding Horizon Curiosity

Matthias Schultheis, Boris Belousov, Hany Abdulsamad, Jan Peters

Comments Published at Conference on Robot Learning (CoRL 2019)

Learning Parametric Constraints in High Dimensions from Demonstrations

Glen Chou, Necmiye Ozay, Dmitry Berenson

Comments 3rd Conference on Robot Learning (CoRL 2019)

Deep Value Model Predictive Control

Farbod Farshidian, David Hoeller, Marco Hutter

Comments Accepted for publication in the Conference on Robotic Learning (CoRL) 2019, Osaka. 10 pages (+5 supplementary)

Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping

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

Comments Conference on Robot Learning (CoRL), 2019

A Learnable Safety Measure

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

Comments 10 pages, Conference on Robot Learning CoRL 2019, 3 figures

Policies Modulating Trajectory Generators

Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans, Vikas Sindhwani, Vincent Vanhoucke

Journal ref In Proceedings of The 2nd Conference on Robot Learning, volume 87 of Proceedings of Machine Learning Research, pages 916-926. PMLR, 29-31 Oct 2018

Disentangled Relational Representations for Explaining and Learning from Demonstration

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

Comments 15 pages, 12 figures, accepted at the Conference on Robot Learning (CoRL) 2019, Osaka, Japan

Kernel Trajectory Maps for Multi-Modal Probabilistic Motion Prediction

Weiming Zhi, Lionel Ott, Fabio Ramos

Comments To appear in Conference on Robot Learning 2019

Learning Compact Models for Planning with Exogenous Processes

Rohan Chitnis, Tomás Lozano-Pérez

Comments CoRL 2019 final version

Leveraging exploration in off-policy algorithms via normalizing flows

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

Comments Accepted to 3rd Conference on Robot Learning (CoRL 2019); Keywords: Exploration, soft actor-critic, normalizing flow, off-policy; maximum entropy, reinforcement learning; deceptive reward; sparse reward; inverse autoregressive flow

Hybrid system identification using switching density networks

Michael Burke, Yordan Hristov, Subramanian Ramamoorthy

Journal ref Conference on Robot Learning (CoRL 2019)

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

Felix Leibfried, Jordi Grau-Moya

Comments Proceedings of the 3rd Conference on Robot Learning (CoRL), Osaka, Japan, 2019

Multimodal Attention Branch Network for Perspective-Free Sentence Generation

Aly Magassouba, Komei Sugiura, Hisashi Kawai

Comments 10 pages, 4 figures. Accepted for CoRL 2019

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

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

Comments Appears in Proceedings of the Third Conference on Robot Learning (CoRL 2019). Companion source code at https://github.com/dti-research/SenseActExperiments/

Risk-Aware Active Inverse Reinforcement Learning

Daniel S. Brown, Yuchen Cui, Scott Niekum

Comments In proceedings of the 2nd Conference on Robot Learning (CoRL) 2018

Particle Filter Networks with Application to Visual Localization

Peter Karkus, David Hsu, Wee Sun Lee

Comments CoRL 2018 camera ready

Neural Modular Control for Embodied Question Answering

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

Comments 10 pages, 3 figures, 2 tables. Published at CoRL 2018. Webpage: https://embodiedqa.org/

Modular meta-learning

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

Comments Presented at CoRL 2018

Deep Drone Racing: Learning Agile Flight in Dynamic Environments

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

Comments Accepted for publication in the Conference on Robotic Learning (CoRL) 2018, Zurich. 10 pages (+3 supplementary)

Journal ref Conference on Robotic Learning (CoRL), 2018