14 citations · 22 across the 10 of their papers we have counts for
11 papers
D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning
Rafael Rafailov, Kyle Hatch, Anikait Singh +9
Offline reinforcement learning algorithms hold the promise of enabling data-driven RL methods that do not require costly or dangerous real-world exploration and benefit from large…
Efficient Imitation Learning with Conservative World Models
Victor Kolev, Rafael Rafailov, Kyle Hatch +2
We tackle the problem of policy learning from expert demonstrations without a reward function. A central challenge in this space is that these policies fail upon deployment due to…
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
Alexander Khazatsky, Karl Pertsch, Suraj Nair +98
The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. Ho…
MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning
Rafael Rafailov, Kyle Hatch, Victor Kolev +3
We study the problem of offline pre-training and online fine-tuning for reinforcement learning from high-dimensional observations in the context of realistic robot tasks. Recent of…
Contrastive Example-Based Control
Kyle Hatch, Benjamin Eysenbach, Rafael Rafailov +4
While many real-world problems that might benefit from reinforcement learning, these problems rarely fit into the MDP mold: interacting with the environment is often expensive and…
Train Offline, Test Online: A Real Robot Learning Benchmark
Gaoyue Zhou, Victoria Dean, Mohan Kumar Srirama +9
Three challenges limit the progress of robot learning research: robots are expensive (few labs can participate), everyone uses different robots (findings do not generalize across l…
Autonomous Attack Mitigation for Industrial Control Systems
John Mern, Kyle Hatch, Ryan Silva +3
Defending computer networks from cyber attack requires timely responses to alerts and threat intelligence. Decisions about how to respond involve coordinating actions across multip…
Interpretable Local Tree Surrogate Policies
John Mern, Sidhart Krishnan, Anil Yildiz +2
High-dimensional policies, such as those represented by neural networks, cannot be reasonably interpreted by humans. This lack of interpretability reduces the trust users have in p…
3D Radar Velocity Maps for Uncertain Dynamic Environments
Ransalu Senanayake, Kyle Beltran Hatch, Jason Zheng +1
Future urban transportation concepts include a mixture of ground and air vehicles with varying degrees of autonomy in a congested environment. In such dynamic environments, occupan…
Reinforcement Learning for Industrial Control Network Cyber Security Orchestration
John Mern, Kyle Hatch, Ryan Silva +2
Defending computer networks from cyber attack requires coordinating actions across multiple nodes based on imperfect indicators of compromise while minimizing disruptions to networ…
Obstacle Avoidance Using a Monocular Camera
Kyle Hatch, John Mern, Mykel Kochenderfer
A collision avoidance system based on simple digital cameras would help enable the safe integration of small UAVs into crowded, low-altitude environments. In this work, we present…