107 citations · 128 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
Self-Supervised Goal-Conditioned Pick and Place
Coline Devin, Payam Rowghanian, Chris Vigorito +2
Robots have the capability to collect large amounts of data autonomously by interacting with objects in the world. However, it is often not obvious \emph{how} to learning from auto…
Grasp2Vec: Learning Object Representations from Self-Supervised Grasping
Eric Jang, Coline Devin, Vincent Vanhoucke +1
Well structured visual representations can make robot learning faster and can improve generalization. In this paper, we study how we can acquire effective object-centric representa…
Deep Object-Centric Representations for Generalizable Robot Learning
Coline Devin, Pieter Abbeel, Trevor Darrell +1
Robotic manipulation in complex open-world scenarios requires both reliable physical manipulation skills and effective and generalizable perception. In this paper, we propose a met…