Search Results for author: Jackie Kay

Found 8 papers, 0 papers with code

Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings

no code implementations9 Dec 2021 Mel Vecerik, Jackie Kay, Raia Hadsell, Lourdes Agapito, Jon Scholz

Dense object tracking, the ability to localize specific object points with pixel-level accuracy, is an important computer vision task with numerous downstream applications in robotics.

Keypoint Detection Object Tracking

Learning Dexterous Manipulation from Suboptimal Experts

no code implementations16 Oct 2020 Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Daniel Zheng, Yuxiang Zhou, Alexandre Galashov, Nicolas Heess, Francesco Nori

Although in many cases the learning process could be guided by demonstrations or other suboptimal experts, current RL algorithms for continuous action spaces often fail to effectively utilize combinations of highly off-policy expert data and on-policy exploration data.

Offline RL Q-Learning

Local Search for Policy Iteration in Continuous Control

no code implementations12 Oct 2020 Jost Tobias Springenberg, Nicolas Heess, Daniel Mankowitz, Josh Merel, Arunkumar Byravan, Abbas Abdolmaleki, Jackie Kay, Jonas Degrave, Julian Schrittwieser, Yuval Tassa, Jonas Buchli, Dan Belov, Martin Riedmiller

We demonstrate that additional computation spent on model-based policy improvement during learning can improve data efficiency, and confirm that model-based policy improvement during action selection can also be beneficial.

Continuous Control

Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation

no code implementations21 Oct 2019 Rae Jeong, Yusuf Aytar, David Khosid, Yuxiang Zhou, Jackie Kay, Thomas Lampe, Konstantinos Bousmalis, Francesco Nori

In this work, we learn a latent state representation implicitly with deep reinforcement learning in simulation, and then adapt it to the real domain using unlabeled real robot data.

Domain Adaptation reinforcement-learning

Modelling Generalized Forces with Reinforcement Learning for Sim-to-Real Transfer

no code implementations21 Oct 2019 Rae Jeong, Jackie Kay, Francesco Romano, Thomas Lampe, Tom Rothorl, Abbas Abdolmaleki, Tom Erez, Yuval Tassa, Francesco Nori

Learning robotic control policies in the real world gives rise to challenges in data efficiency, safety, and controlling the initial condition of the system.

reinforcement-learning

Robust Reinforcement Learning for Continuous Control with Model Misspecification

no code implementations ICLR 2020 Daniel J. Mankowitz, Nir Levine, Rae Jeong, Yuanyuan Shi, Jackie Kay, Abbas Abdolmaleki, Jost Tobias Springenberg, Timothy Mann, Todd Hester, Martin Riedmiller

We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into continuous control Reinforcement Learning (RL) algorithms.

Continuous Control reinforcement-learning

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