abstract: Recent advancements in external memory based neural networks have shown promise
in solving tasks that require precise storage and retrieval of past information. Re-
searchers have applied these models to a wide range of tasks that have algorithmic
properties but have not applied these models to real-world robotic tasks. In this
thesis, we present memory-augmented neural networks that synthesize robot navigation policies which a) encode long-term temporal dependencies b) make decisions in
partially observed environments and c) quantify the uncertainty inherent in the task.
We extract information about the temporal structure of a task via imitation learning
from human demonstration and evaluate the performance of the models on control
policies for a robot navigation task. Experiments are performed in partially observed
environments in both simulation and the real world / Dissertation/Thesis / Masters Thesis Computer Science 2018
| Identifer | oai:union.ndltd.org:asu.edu/item:51746 |
| Date | January 2018 |
| Contributors | Srivatsav, Nambi (Author), Ben Amor, Hani (Advisor), Srivastava, Siddharth (Committee member), Tong, HangHang (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 29 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/ |
Page generated in 0.0019 seconds