Papers
Towards Learning to Detect and Predict Contact Events on Vision-based Tactile Sensors
Yazhan Zhang, Weihao Yuan, Zicheng Kan et al.
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 et al.
An Online Learning Procedure for Feedback Linearization Control without Torque Measurements
M. Capotondi, G. Turrisi, C. Gaz et al.
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 et al.
Certified Adversarial Robustness for Deep Reinforcement Learning
Björn Lütjens, Michael Everett, Jonathan P. How
Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments
Martin Weiss, Simon Chamorro, Roger Girgis et al.
ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots
Michael Ahn, Henry Zhu, Kristian Hartikainen et al.
Learning to Generalize Kinematic Models to Novel Objects
Ben Abbatematteo, Stefanie Tellex, George Konidaris
Receding Horizon Curiosity
Matthias Schultheis, Boris Belousov, Hany Abdulsamad et al.
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 et al.
Variational Optimization Based Reinforcement Learning for Infinite Dimensional Stochastic Systems
Ethan N. Evans, Marcus A. Periera, George I. Boutselis et al.
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 et al.
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 et al.
Active Domain Randomization
Bhairav Mehta, Manfred Diaz, Florian Golemo et al.
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 et al.
Learning Latent Plans from Play
Corey Lynch, Mohi Khansari, Ted Xiao et al.
Deep Dynamics Models for Learning Dexterous Manipulation
Anusha Nagabandi, Kurt Konolige, Sergey Levine et al.
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He et al.
Worst Cases Policy Gradients
Yichuan Charlie Tang, Jian Zhang, Ruslan Salakhutdinov