39 citations · 148 across the 14 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…
Evaluating model-based planning and planner amortization for continuous control
Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim +8
There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches. In this paper we attempt to evaluate this i…
Learning Dexterous Manipulation from Suboptimal Experts
Rae Jeong, Jost Tobias Springenberg, Jackie Kay +5
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 le…
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models
Arunkumar Byravan, Jost Tobias Springenberg, Abbas Abdolmaleki +6
Humans are masters at quickly learning many complex tasks, relying on an approximate understanding of the dynamics of their environments. In much the same way, we would like our le…