Training Robot Policies using External Memory Based Networks Via Imitation Learning

• Other Authors: Srivatsav, Nambi (Author)
• Format: Dissertation
• Language: English
• Published: 2018
• Subjects: Computer science; Imitation Learning; Memory Networks; Neural Network; Neural Turing Machines; Robot Policies; Uncertainity Estimation
• Online Access: http://hdl.handle.net/2286/R.I.51746

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