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3rd CoRL 2019: Osaka, Japan
- Leslie Pack Kaelbling, Danica Kragic, Komei Sugiura:
3rd Annual Conference on Robot Learning, CoRL 2019, Osaka, Japan, October 30 - November 1, 2019, Proceedings. Proceedings of Machine Learning Research 100, PMLR 2019 - Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Tingnan Zhang, Jie Tan, Vikas Sindhwani:
Data Efficient Reinforcement Learning for Legged Robots. 1-10 - Youngwoon Lee, Edward S. Hu, Zhengyu Yang, Joseph J. Lim:
To Follow or not to Follow: Selective Imitation Learning from Observations. 11-23 - Ashwin Balakrishna, Brijen Thananjeyan, Jonathan Lee, Felix Li, Arsh Zahed, Joseph E. Gonzalez, Ken Goldberg:
On-Policy Robot Imitation Learning from a Converging Supervisor. 24-41 - Kuan Fang, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei:
Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation. 42-52 - Yuzhe Qin, Rui Chen, Hao Zhu, Meng Song, Jing Xu, Hao Su:
S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes. 53-65 - Dian Chen, Brady Zhou, Vladlen Koltun, Philipp Krähenbühl:
Learning by Cheating. 66-75 - Aly Magassouba, Komei Sugiura, Hisashi Kawai:
Multimodal Attention Branch Network for Perspective-Free Sentence Generation. 76-85 - Yuning Chai, Benjamin Sapp, Mayank Bansal, Dragomir Anguelov:
MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction. 86-99 - Yufei Ye, Dhiraj Gandhi, Abhinav Gupta, Shubham Tulsiani:
Object-centric Forward Modeling for Model Predictive Control. 100-109 - Ofir Nachum, Michael Ahn, Hugo Ponte, Shixiang Shane Gu, Vikash Kumar:
Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real. 110-121 - Siddharth Ancha, Junyu Nan, David Held:
Combining Deep Learning and Verification for Precise Object Instance Detection. 122-141 - Bohan Wu, Iretiayo Akinola, Jacob Varley, Peter K. Allen:
MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning. 142-161 - Sarah Bechtle, Yixin Lin, Akshara Rai, Ludovic Righetti, Franziska Meier:
Curious iLQR: Resolving Uncertainty in Model-based RL. 162-171 - Michael Burke, Yordan Hristov, Subramanian Ramamoorthy:
Hybrid system identification using switching density networks. 172-181 - Rinu Boney, Juho Kannala, Alexander Ilin:
Regularizing Model-Based Planning with Energy-Based Models. 182-191 - Vaibhav V. Unhelkar, Shen Li, Julie A. Shah:
Semi-Supervised Learning of Decision-Making Models for Human-Robot Collaboration. 192-203 - Mustafa Mukadam, Ching-An Cheng, Dieter Fox, Byron Boots, Nathan D. Ratliff:
Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping. 204-219 - Keuntaek Lee, Gabriel Nakajima An, Viacheslav Zakharov, Evangelos A. Theodorou:
Perceptual Attention-based Predictive Control. 220-232 - Noémie Jaquier, Leonel Dario Rozo, Sylvain Calinon, Mathias Bürger:
Bayesian Optimization Meets Riemannian Manifolds in Robot Learning. 233-246 - Noémie Jaquier, David Ginsbourger, Sylvain Calinon:
Learning from demonstration with model-based Gaussian process. 247-257 - Masashi Okada, Tadahiro Taniguchi:
Variational Inference MPC for Bayesian Model-based Reinforcement Learning. 258-272 - Lukas Schwenkel, Meng Guo, Mathias Bürger:
Optimizing Sequences of Probabilistic Manipulation Skills Learned from Demonstration. 273-282 - Meng Guo, Mathias Bürger:
Predictive Safety Network for Resource-constrained Multi-agent Systems. 283-292 - Leonidas Koutras, Zoe Doulgeri:
A correct formulation for the Orientation Dynamic Movement Primitives for robot control in the Cartesian space. 293-302 - Dan Barnes, Rob Weston, Ingmar Posner:
Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information. 303-316 - Zhaoming Xie, Patrick Clary, Jeremy Dao, Pedro Morais, Jonathan W. Hurst, Michiel van de Panne:
Learning Locomotion Skills for Cassie: Iterative Design and Sim-to-Real. 317-329 - Daniel S. Brown, Wonjoon Goo, Scott Niekum:
Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations. 330-359 - Felix Leibfried, Jordi Grau-Moya:
Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning. 360-373 - Yilun Du, Toru Lin, Igor Mordatch:
Model-Based Planning with Energy-Based Models. 374-383 - Kelvin Wong, Shenlong Wang, Mengye Ren, Ming Liang, Raquel Urtasun:
Identifying Unknown Instances for Autonomous Driving. 384-393 - Jesse Thomason, Michael Murray, Maya Cakmak, Luke Zettlemoyer:
Vision-and-Dialog Navigation. 394-406 - Ajay Jain, Sergio Casas, Renjie Liao, Yuwen Xiong, Song Feng, Sean Segal, Raquel Urtasun:
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction. 407-419 - Somil Bansal, Varun Tolani, Saurabh Gupta, Jitendra Malik, Claire J. Tomlin:
Combining Optimal Control and Learning for Visual Navigation in Novel Environments. 420-429 - Bogdan Mazoure, Thang Doan, Audrey Durand, Joelle Pineau, R. Devon Hjelm:
Leveraging exploration in off-policy algorithms via normalizing flows. 430-444 - Adam Allevato, Elaine Schaertl Short, Mitch Pryor, Andrea Thomaz:
TuneNet: One-Shot Residual Tuning for System Identification and Sim-to-Real Robot Task Transfer. 445-455 - Rika Antonova, Akshara Rai, Tianyu Li, Danica Kragic:
Bayesian Optimization in Variational Latent Spaces with Dynamic Compression. 456-465 - Nicolai A. Lynnerup, Laura Nolling, Rasmus Hasle, John Hallam:
A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots. 466-489 - Matthew Wilson, Tucker Hermans:
Learning to Manipulate Object Collections Using Grounded State Representations. 490-502 - Vitor Guizilini, Jie Li, Rares Ambrus, Sudeep Pillai, Adrien Gaidon:
Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances. 503-512 - Pascal Klink, Hany Abdulsamad, Boris Belousov, Jan Peters:
Self-Paced Contextual Reinforcement Learning. 513-529 - Ashvin Nair, Shikhar Bahl, Alexander Khazatsky, Vitchyr Pong, Glen Berseth, Sergey Levine:
Contextual Imagined Goals for Self-Supervised Robotic Learning. 530-539 - Junha Roh, Chris Paxton, Andrzej Pronobis, Ali Farhadi, Dieter Fox:
Conditional Driving from Natural Language Instructions. 540-551 - Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann, Chun-Yi Lee
:
Adversarial Active Exploration for Inverse Dynamics Model Learning. 552-565 - Arunkumar Byravan, Jost Tobias Springenberg, Abbas Abdolmaleki, Roland Hafner, Michael Neunert, Thomas Lampe, Noah Y. Siegel, Nicolas Heess, Martin A. Riedmiller:
Imagined Value Gradients: Model-Based Policy Optimization with Tranferable Latent Dynamics Models. 566-589 - Iou-Jen Liu, Raymond A. Yeh, Alexander G. Schwing:
PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning. 590-602 - Chengshu Li, Fei Xia, Roberto Martín-Martín, Silvio Savarese:
HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators. 603-616 - Ashish Kumar, Saurabh Gupta, Jitendra Malik:
Learning Navigation Subroutines from Egocentric Videos. 617-626 - Steve Heim, Alexander von Rohr
A Learnable Safety Measure. 627-639 - Michael Lutter, Boris Belousov, Kim Listmann, Debora Clever, Jan Peters:
HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints. 640-650 - Eunah Jung, Nan Yang, Daniel Cremers:
Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light. 651-660 - Juntong Lin, Xuyun Yang, Peiwei Zheng, Hui Cheng:
Connectivity Guaranteed Multi-robot Navigation via Deep Reinforcement Learning. 661-670 - Ekaterina I. Tolstaya, Fernando Gama, James Paulos, George J. Pappas, Vijay Kumar, Alejandro Ribeiro:
Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks. 671-682 - Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Deepali Jain, Yuxiang Yang, Atil Iscen, Jasmine Hsu, Vikas Sindhwani:
Provably Robust Blackbox Optimization for Reinforcement Learning. 683-696 - Joe Watson, Hany Abdulsamad, Jan Peters:
Stochastic Optimal Control as Approximate Input Inference. 697-716 - Andrey Kurenkov, Ajay Mandlekar, Roberto Martin Martin, Silvio Savarese, Animesh Garg:
AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers. 717-734 - Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier, Thomas Lampe, Jost Tobias Springenberg, Roland Hafner, Francesco Romano, Jonas Buchli, Nicolas Heess, Martin A. Riedmiller:
Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics. 735-751 - Dylan P. Losey, Mengxi Li, Jeannette Bohg, Dorsa Sadigh:
Learning from My Partner's Actions: Roles in Decentralized Robot Teams. 752-765 - Minghan Wei, Volkan Isler:
Energy-efficient Path Planning for Ground Robots by and Combining Air and Ground Measurements. 766-775 - Orr Krupnik, Igor Mordatch, Aviv Tamar:
Multi-Agent Reinforcement Learning with Multi-Step Generative Models. 776-790 - Alexander Sax, Jeffrey O. Zhang, Bradley Emi, Amir Zamir, Silvio Savarese, Leonidas J. Guibas, Jitendra Malik:
Learning to Navigate Using Mid-Level Visual Priors. 791-812 - Rohan Chitnis, Tomás Lozano-Pérez:
Learning Compact Models for Planning with Exogenous Processes. 813-822 - Arbaaz Khan, Ekaterina I. Tolstaya, Alejandro Ribeiro, Vijay Kumar:
Graph Policy Gradients for Large Scale Robot Control. 823-834 - Rémy Portelas, Cédric Colas, Katja Hofmann, Pierre-Yves Oudeyer:
Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments. 835-853 - Kevin Sebastian Luck, Heni Ben Amor, Roberto Calandra:
Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning. 854-869 - Yordan Hristov, Daniel Angelov, Michael Burke, Alex Lascarides, Subramanian Ramamoorthy:
Disentangled Relational Representations for Explaining and Learning from Demonstration. 870-884 - Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, Chelsea Finn:
RoboNet: Large-Scale Multi-Robot Learning. 885-897 - Yuxiao Chen, Sumanth Dathathri, Tung Phan-Minh, Richard M. Murray:
Counter-example Guided Learning of Bounds on Environment Behavior. 898-909 - Swaminathan Gurumurthy, Sumit Kumar, Katia P. Sycara:
MAME : Model-Agnostic Meta-Exploration. 910-922 - Yin Zhou, Pei Sun, Yu Zhang, Dragomir Anguelov, Jiyang Gao, Tom Ouyang, James Guo, Jiquan Ngiam, Vijay Vasudevan:
End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds. 923-932 - Michael Noseworthy, Rohan Paul, Subhro Roy, Daehyung Park, Nicholas Roy:
Task-Conditioned Variational Autoencoders for Learning Movement Primitives. 933-944 - Devesh K. Jha, Arvind U. Raghunathan, Diego Romeres:
Quasi-Newton Trust Region Policy Optimization. 945-954 - Beomjoon Kim, Luke Shimanuki:
Learning value functions with relational state representations for guiding task-and-motion planning. 955-968 - Grady R. Williams, Brian Goldfain, Keuntaek Lee, Jason Gibson, James M. Rehg, Evangelos A. Theodorou:
Locally Weighted Regression Pseudo-Rehearsal for Adaptive Model Predictive Control. 969-978 - Maximilian Sieb, Xian Zhou, Audrey Huang, Oliver Kroemer, Katerina Fragkiadaki:
Graph-Structured Visual Imitation. 979-989 - David Hoeller, Farbod Farshidian, Marco Hutter:
Deep Value Model Predictive Control. 990-1004 - Daehyung Park, Michael Noseworthy, Rohan Paul, Subhro Roy, Nicholas Roy:
Inferring Task Goals and Constraints using Bayesian Nonparametric Inverse Reinforcement Learning. 1005-1014 - Yen-Chen Lin, Maria Bauzá, Phillip Isola:
Experience-Embedded Visual Foresight. 1015-1024 - Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, Karol Hausman:
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning. 1025-1037 - Sandy H. Huang, Isabella Huang, Ravi Pandya, Anca D. Dragan:
Nonverbal Robot Feedback for Human Teachers. 1038-1051 - Rares Ambrus, Vitor Guizilini, Jie Li, Sudeep Pillai, Adrien Gaidon:
Two Stream Networks for Self-Supervised Ego-Motion Estimation. 1052-1061 - Alan Wu, A. J. Piergiovanni, Michael S. Ryoo:
Model-based Behavioral Cloning with Future Image Similarity Learning. 1062-1077 - Yichuan Charlie Tang, Jian Zhang, Ruslan Salakhutdinov:
Worst Cases Policy Gradients. 1078-1093 - Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, Sergey Levine:
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning. 1094-1100 - Anusha Nagabandi, Kurt Konolige, Sergey Levine, Vikash Kumar:
Deep Dynamics Models for Learning Dexterous Manipulation. 1101-1112 - Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, Pierre Sermanet:
Learning Latent Plans from Play. 1113-1132 - Chaitanya Mitash, Bowen Wen, Kostas E. Bekris, Abdeslam Boularias:
Scene-level Pose Estimation for Multiple Instances of Densely Packed Objects. 1133-1145 - Yuchen Xiao, Joshua Hoffman, Christopher Amato:
Macro-Action-Based Deep Multi-Agent Reinforcement Learning. 1146-1161 - Bhairav Mehta, Manfred Diaz, Florian Golemo, Christopher J. Pal, Liam Paull:
Active Domain Randomization. 1162-1176 - Erdem Biyik, Malayandi Palan, Nicholas C. Landolfi, Dylan P. Losey, Dorsa Sadigh:
Asking Easy Questions: A User-Friendly Approach to Active Reward Learning. 1177-1190 - Jieliang Luo, Hui Li:
Dynamic Experience Replay. 1191-1200 - Siddharth Patki, Ethan Fahnestock, Thomas M. Howard, Matthew R. Walter:
Language-guided Semantic Mapping and Mobile Manipulation in Partially Observable Environments. 1201-1210 - Glen Chou, Necmiye Ozay, Dmitry Berenson:
Learning Parametric Constraints in High Dimensions from Demonstrations. 1211-1230 - Ethan N. Evans, Marcus A. Pereira, George I. Boutselis, Evangelos A. Theodorou:
Variational Optimization Based Reinforcement Learning for Infinite Dimensional Stochastic Systems. 1231-1246 - Akanksha Saran, Elaine Schaertl Short, Andrea Thomaz, Scott Niekum:
Understanding Teacher Gaze Patterns for Robot Learning. 1247-1258 - Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel, Shixiang Gu:
A Divergence Minimization Perspective on Imitation Learning Methods. 1259-1277 - Matthias Schultheis, Boris Belousov, Hany Abdulsamad, Jan Peters:
Receding Horizon Curiosity. 1278-1288 - Ben Abbatematteo, Stefanie Tellex, George Konidaris:
Learning to Generalize Kinematic Models to Novel Objects. 1289-1299 - Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte, Abhishek Gupta, Sergey Levine, Vikash Kumar:
ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots. 1300-1313 - Martin Weiss, Simon Chamorro, Roger Girgis, Margaux Luck, Samira Ebrahimi Kahou, Joseph Paul Cohen, Derek Nowrouzezahrai, Doina Precup, Florian Golemo, Chris Pal:
Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments. 1314-1327 - Björn Lütjens, Michael Everett, Jonathan P. How:
Certified Adversarial Robustness for Deep Reinforcement Learning. 1328-1337 - Yunzhi Zhang, Ignasi Clavera, Boren Tsai, Pieter Abbeel:
Asynchronous Methods for Model-Based Reinforcement Learning. 1338-1347 - Brian Delhaisse, Leonel Dario Rozo, Darwin G. Caldwell:
PyRoboLearn: A Python Framework for Robot Learning Practitioners. 1348-1358 - Marco Capotondi, Giulio Turrisi, Claudio Gaz, Valerio Modugno, Giuseppe Oriolo, Alessandro De Luca:
An Online Learning Procedure for Feedback Linearization Control without Torque Measurements. 1359-1368 - Christopher Xie, Yu Xiang, Arsalan Mousavian, Dieter Fox:
The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentation. 1369-1378 - Ching-An Cheng, Xinyan Yan, Byron Boots:
Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods. 1379-1394 - Yazhan Zhang, Weihao Yuan, Zicheng Kan, Michael Yu Wang:
Towards Learning to Detect and Predict Contact Events on Vision-based Tactile Sensors. 1395-1404 - Weiming Zhi, Lionel Ott, Fabio Ramos:
Kernel Trajectory Maps for Multi-Modal Probabilistic Motion Prediction. 1405-1414 - Valts Blukis, Yannick Terme, Eyvind Niklasson, Ross A. Knepper, Yoav Artzi:
Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated Flight. 1415-1438 - Rishi Veerapaneni, John D. Co-Reyes, Michael Chang, Michael Janner, Chelsea Finn, Jiajun Wu, Joshua B. Tenenbaum, Sergey Levine:
Entity Abstraction in Visual Model-Based Reinforcement Learning. 1439-1456 - Muhammad Asif Rana, Anqi Li, Harish Ravichandar, Mustafa Mukadam, Sonia Chernova, Dieter Fox, Byron Boots, Nathan D. Ratliff:
Learning Reactive Motion Policies in Multiple Task Spaces from Human Demonstrations. 1457-1468
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