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Rae Jeong

5 accepted papers

2021

Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes

Alex X. Lee, Coline Manon Devin, Yuxiang Zhou, Thomas Lampe, Konstantinos Bousmalis, Jost Tobias Springenberg +15
CoRL 2021poster

We study the problem of robotic stacking with objects of complex geometry. We propose a challenging and diverse set of such objects that was carefully designed to require strategies beyond a simple “pick-and-place” solution. Our method is a reinforcement learning (RL) approach combined with vision-b…

Cited by 118Source ↗code ↗Scholar ↗
2020

Learning Dexterous Manipulation from Suboptimal Experts

Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Dan Zheng, Alexandre Galashov, Nicolas Heess +1
CoRL 2020

Learning dexterous manipulation in high-dimensional state-action spaces is an important open challenge with exploration presenting a major bottleneck. Although in many cases the learning process could be guided by demonstrations or other suboptimal experts, current RL algorithms for continuous actio

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2020

Robust Reinforcement Learning for Continuous Control with Model Misspecification

Daniel J. Mankowitz, Nir Levine, Rae Jeong, Abbas Abdolmaleki, Jost Tobias Springenberg, Yuanyuan Shi +4
ICLR 2020poster

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. We specifically focus on incorporating robustness into a state-of-the-art continuous contr…

Cited by 135Source ↗Scholar ↗
2020

Scaling data-driven robotics with reward sketching and batch reinforcement learning

Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushova, Scott Reed, Rae Jeong +10
RSS 2020poster

By harnessing a growing dataset of robot experience, we learn control policies for a diverse and increasing set of related manipulation tasks. To make this possible, we introduce reward sketching: an effective way of eliciting human preferences to learn the reward function for a new task. This rewar…

Cited by 168Source ↗PDF ↗code ↗Scholar ↗
2020

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

Rae Jeong, Yusuf Aytar, David Khosid, Yuxiang Zhou, Jackie Kay, Thomas Lampe +2
ICRA 2020poster

Collecting and automatically obtaining reward signals from real robotic visual data for the purposes of training reinforcement learning algorithms can be quite challenging and time-consuming. Methods for utilizing unlabeled data can have a huge potential to further accelerate robotic learning. We co…

Cited by 78Source ↗Scholar ↗