2017 IJCAI IJCAI 2017

Teaching Robots through Situated Interactive Dialogue and Visual Demonstrations

Abstract

The ability to quickly adapt to new environments and incorporate new knowledge is of great importance for robots operating in unstructured environments and interacting with non-expert users. This paper reports on our current progress in tackling this problem. We propose the development of a framework for teaching robots to perform tasks using natural language instructions, visual demonstrations and interactive dialogue. Moreover, we present a module for learning objects incrementally and on-the-fly that would enable robots to ground referents in the natural language instructions and reason about the state of the world.

📈 Trend Setter — Intent Classification
🧭 Keyword Pioneer — grounded dialogue
🐣 Hot Topic Early Bird — robot learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics