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Nick Walker

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JAIR Journal 2020 Journal Article

Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog

Jesse Thomason
Aishwarya Padmakumar
Jivko Sinapov
Nick Walker
Yuqian Jiang
Harel Yedidsion
Justin Hart
Peter Stone

In this work, we present methods for using human-robot dialog to improve language understanding for a mobile robot agent. The agent parses natural language to underlying semantic meanings and uses robotic sensors to create multi-modal models of perceptual concepts like red and heavy. The agent can be used for showing navigation routes, delivering objects to people, and relocating objects from one location to another. We use dialog clari_cation questions both to understand commands and to generate additional parsing training data. The agent employs opportunistic active learning to select questions about how words relate to objects, improving its understanding of perceptual concepts. We evaluated this agent on Amazon Mechanical Turk. After training on data induced from conversations, the agent reduced the number of dialog questions it asked while receiving higher usability ratings. Additionally, we demonstrated the agent on a robotic platform, where it learned new perceptual concepts on the y while completing a real-world task.

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AAAI Conference 2017 Conference Paper

Automatic Curriculum Graph Generation for Reinforcement Learning Agents

Maxwell Svetlik
Matteo Leonetti
Jivko Sinapov
Rishi Shah
Nick Walker
Peter Stone

In recent years, research has shown that transfer learning methods can be leveraged to construct curricula that sequence a series of simpler tasks such that performance on a ﬁnal target task is improved. A major limitation of existing approaches is that such curricula are handcrafted by humans that are typically domain experts. To address this limitation, we introduce a method to generate a curriculum based on task descriptors and a novel metric of transfer potential. Our method automatically generates a curriculum as a directed acyclic graph (as opposed to a linear sequence as done in existing work). Experiments in both discrete and continuous domains show that our method produces curricula that improve the agent’s learning performance when compared to the baseline condition of learning on the target task from scratch.

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