112 citations · 129 across the 3 of their papers we have counts for
7 papers
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 +4
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 environ…
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes
Alex X. Lee, Coline Devin, Yuxiang Zhou +18
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 strategie…
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
Alex X. Lee, Anusha Nagabandi, Pieter Abbeel +1
Deep reinforcement learning (RL) algorithms can use high-capacity deep networks to learn directly from image observations. However, these high-dimensional observation spaces presen…
Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control
Frederik Ebert, Chelsea Finn, Sudeep Dasari +3
Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw sensory inputs, but have yet to achieve the kind of broad generalization and applicability dem…
Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning
Frederik Ebert, Sudeep Dasari, Alex X. Lee +2
Prediction is an appealing objective for self-supervised learning of behavioral skills, particularly for autonomous robots. However, effectively utilizing predictive models for con…
Stochastic Adversarial Video Prediction
Alex X. Lee, Richard Zhang, Frederik Ebert +3
Being able to predict what may happen in the future requires an in-depth understanding of the physical and causal rules that govern the world. A model that is able to do so has a n…