Papers
Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor et al.
Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation
Rika Antonova, Mia Kokic, Johannes A. Stork et al.
Reinforcement Learning of Phase Oscillators for Fast Adaptation to Moving Targets
Guilherme Maeda, Okan Koc, Jun Morimoto
Model-Based Reinforcement Learning via Meta-Policy Optimization
Ignasi Clavera, Jonas Rothfuss, John Schulman et al.
Learning to Localize Using a LiDAR Intensity Map
Ioan Andrei Barsan, Shenlong Wang, Andrei Pokrovsky et al.
Learning Neural Parsers with Deterministic Differentiable Imitation Learning
Tanmay Shankar, Nicholas Rhinehart, Katharina Muelling et al.
Benchmarking Reinforcement Learning Algorithms on Real-World Robots
A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan et al.
HybridNet: Integrating Model-based and Data-driven Learning to Predict Evolution of Dynamical Systems
Yun Long, Xueyuan She, Saibal Mukhopadhyay
Learning Audio Feedback for Estimating Amount and Flow of Granular Material
Samuel Clarke, Travers Rhodes, Christopher G. Atkeson et al.
Batch Active Preference-Based Learning of Reward Functions
Erdem Biyik, Dorsa Sadigh
Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction
Valts Blukis, Dipendra Misra, Ross A. Knepper et al.
Curiosity Driven Exploration of Learned Disentangled Goal Spaces
Adrien Laversanne-Finot, Alexandre Pere, Pierre-Yves Oudeyer
Learning 6-DoF Grasping and Pick-Place Using Attention Focus
Marcus Gualtieri, Robert Platt
The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems
Spencer M. Richards, Felix Berkenkamp, Andreas Krause
Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization
Paul-Edouard Sarlin, Frederic Debraine, Marcin Dymczyk et al.
Automorphing Kernels for Nonstationarity in Mapping Unstructured Environments
Ransalu Senanayake, Anthony Tompkins, Fabio Ramos
Adaptable replanning with compressed linear action models for learning from demonstrations
Clement Gehring, Leslie Pack Kaelbling, Tomas Lozano-Perez
Reinforcement Learning of Active Vision for Manipulating Objects under Occlusions
Ricson Cheng, Arpit Agarwal, Katerina Fragkiadaki
Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation
Fan Wang, Bo Zhou, Ke Chen et al.
Benchmarks for reinforcement learning in mixed-autonomy traffic
Eugene Vinitsky, Aboudy Kreidieh, Luc Le Flem et al.
Bayesian RL for Goal-Only Rewards
Philippe Morere, Fabio Ramos
Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation
Peter R. Florence, Lucas Manuelli, Russ Tedrake
Risk-Aware Active Inverse Reinforcement Learning
Daniel S. Brown, Yuchen Cui, Scott Niekum
Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal
Jake Bruce, Niko Sunderhauf, Piotr Mirowski et al.
A Data-Efficient Approach to Precise and Controlled Pushing
Maria Bauza, Francois R. Hogan, Alberto Rodriguez