254 citations · 1.4k across the 100 of their papers we have counts for
5 papers · 1 filter
Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning
Richard Liaw, Sanjay Krishnan, Animesh Garg +3
Rather than learning new control policies for each new task, it is possible, when tasks share some structure, to compose a "meta-policy" from previously learned policies. This pape…
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
Danfei Xu, Suraj Nair, Yuke Zhu +4
In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program in…
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image
Andrey Kurenkov, Jingwei Ji, Animesh Garg +4
3D reconstruction from a single image is a key problem in multiple applications ranging from robotic manipulation to augmented reality. Prior methods have tackled this problem thro…
ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems
James Harrison, Animesh Garg, Boris Ivanovic +4
Model-free policy learning has enabled robust performance of complex tasks with relatively simple algorithms. However, this simplicity comes at the cost of requiring an Oracle and…
Weakly supervised 3D Reconstruction with Adversarial Constraint
JunYoung Gwak, Christopher B. Choy, Animesh Garg +2
Supervised 3D reconstruction has witnessed a significant progress through the use of deep neural networks. However, this increase in performance requires large scale annotations of…