AIConfPaper
← Search

Alex X. Lee

10 accepted papers

2022

How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation

Alex X. Lee, Coline Devin, Jost Tobias Springenberg, Yuxiang Zhou, Thomas Lampe, Abbas Abdolmaleki +1
IROS 2022poster

Reinforcement learning (RL) has been shown to be effective at learning control from experience. However, RL typically requires a large amount of online interaction with the environment. This limits its applicability to real-world settings, such as in robotics, where such interaction is expensive. In…

Cited by 20Source ↗Scholar ↗
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

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine
NeurIPS 2020poster

Deep reinforcement learning (RL) algorithms can use high-capacity deep networks to learn directly from image observations. However, these high-dimensional observation spaces present a number of challenges in practice, since the policy must now solve two problems: representation learning and task le…

Cited by 484Source ↗PDF ↗Scholar ↗
2018

Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning

Frederik Ebert, Sudeep Dasari, Alex X. Lee, Sergey Levine, Chelsea Finn
CoRL 2018

Prediction is an appealing objective for self-supervised learning of behavioral skills, particularly for autonomous robots. However, effectively utilizing predictive models for control, especially with raw image inputs, poses a number of major challenges. How should the predictions be used? What hap

Cited by 0Source ↗PDF ↗Scholar ↗
2015

A non-rigid point and normal registration algorithm with applications to learning from demonstrations

Alex X. Lee, Max A. Goldstein, Shane T. Barratt, Pieter Abbeel
ICRA 2015poster

Recent work [1], [2], [3] has shown promising results in learning from demonstrations for the manipulation of deformable objects. Their approach finds a non-rigid registration between points in the demonstration scene and points in the test scene. This registration is then extrapolated and applied t…

Cited by 15Source ↗Scholar ↗
2015

Beyond lowest-warping cost action selection in trajectory transfer

Dylan Hadfield-Menell, Alex X. Lee, Chelsea Finn, Eric Tzeng, Sandy Huang, Pieter Abbeel
ICRA 2015poster

We consider the problem of learning from demonstrations to manipulate deformable objects. Recent work [1], [2], [3] has shown promising results that enable robotic manipulation of deformable objects through learning from demonstrations. Their approach is able to generalize from a single demonstratio…

Cited by 9Source ↗Scholar ↗
2015

Learning force-based manipulation of deformable objects from multiple demonstrations

Alex X. Lee, Henry Lu, Abhishek Gupta, Sergey Levine, Pieter Abbeel
ICRA 2015poster

Manipulation of deformable objects often requires a robot to apply specific forces to bring the object into the desired configuration. For instance, tightening a knot requires pulling on the ends, flattening an article of clothing requires smoothing out wrinkles, and erasing a whiteboard requires ap…

Cited by 185Source ↗Scholar ↗
2015

Learning from multiple demonstrations using trajectory-aware non-rigid registration with applications to deformable object manipulation

Alex X. Lee, Abhishek Gupta, Henry Lu, Sergey Levine, Pieter Abbeel
IROS 2015poster

Learning from demonstration by means of non-rigid point cloud registration is an effective tool for learning to manipulate a wide range of deformable objects. However, most methods that use non-rigid registration to transfer demonstrated trajectories assume that the test and demonstration scene are…

Cited by 49Source ↗Scholar ↗