no code implementations • 6 Jan 2024 • Rafael Rafailov, Kyle Hatch, Victor Kolev, John D. Martin, Mariano Phielipp, Chelsea Finn
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.
1 code implementation • 24 Jul 2023 • Kyle Hatch, Benjamin Eysenbach, Rafael Rafailov, Tianhe Yu, Ruslan Salakhutdinov, Sergey Levine, Chelsea Finn
In this paper, we propose a method for offline, example-based control that learns an implicit model of multi-step transitions, rather than a reward function.
1 code implementation • 1 Jun 2023 • Gaoyue Zhou, Victoria Dean, Mohan Kumar Srirama, Aravind Rajeswaran, Jyothish Pari, Kyle Hatch, Aryan Jain, Tianhe Yu, Pieter Abbeel, Lerrel Pinto, Chelsea Finn, Abhinav Gupta
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 labs), and we lack internet-scale robotics data.
no code implementations • 3 Nov 2021 • John Mern, Kyle Hatch, Ryan Silva, Cameron Hickert, Tamim Sookoor, Mykel J. Kochenderfer
The proposed deep reinforcement learning approach outperforms a fully automated playbook method in simulation, taking less disruptive actions while also defending more nodes on the network.
no code implementations • 16 Sep 2021 • John Mern, Sidhart Krishnan, Anil Yildiz, Kyle Hatch, Mykel J. Kochenderfer
In this work, we propose a method to build predictable policy trees as surrogates for policies such as neural networks.
no code implementations • 9 Jun 2021 • John Mern, Kyle Hatch, Ryan Silva, Jeff Brush, Mykel J. Kochenderfer
Defending computer networks from cyber attack requires coordinating actions across multiple nodes based on imperfect indicators of compromise while minimizing disruptions to network operations.
no code implementations • 3 Dec 2020 • Kyle Hatch, John Mern, Mykel Kochenderfer
In this work, we present an obstacle avoidance system for small UAVs that uses a monocular camera with a hybrid neural network and path planner controller.