251 citations · 756 across the 10 of their papers we have counts for
10 papers
LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement
Haonan Chang, Kai Gao, Kowndinya Boyalakuntla +5
We introduce a novel approach to the executable semantic object rearrangement problem. In this challenge, a robot seeks to create an actionable plan that rearranges objects within…
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao +36
The ability to leverage heterogeneous robotic experience from different robots and tasks to quickly master novel skills and embodiments has the potential to transform robot learnin…
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…
Self-Supervised Visual Planning with Temporal Skip Connections
Frederik Ebert, Chelsea Finn, Alex X. Lee +1
In order to autonomously learn wide repertoires of complex skills, robots must be able to learn from their own autonomously collected data, without human supervision. One learning…
Learning Visual Servoing with Deep Features and Fitted Q-Iteration
Alex X. Lee, Sergey Levine, Pieter Abbeel
Visual servoing involves choosing actions that move a robot in response to observations from a camera, in order to reach a goal configuration in the world. Standard visual servoing…