116 citations · 458 across the 20 of their papers we have counts for
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Deep Imitative Models for Flexible Inference, Planning, and Control
Nicholas Rhinehart, Rowan McAllister, Sergey Levine
Imitation Learning (IL) is an appealing approach to learn desirable autonomous behavior. However, directing IL to achieve arbitrary goals is difficult. In contrast, planning-based…
Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information
Arjun Sharma, Mohit Sharma, Nicholas Rhinehart +1
The use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging. In fact, previous work has shown tha…
Learning Neural Parsers with Deterministic Differentiable Imitation Learning
Tanmay Shankar, Nicholas Rhinehart, Katharina Muelling +1
We explore the problem of learning to decompose spatial tasks into segments, as exemplified by the problem of a painting robot covering a large object. Inspired by the ability of c…