ICML 2019
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ICML Workshop on Imitation, Intent, and Interaction (I3)

Nicholas Rhinehart · Sergey Levine · Chelsea Finn · He He · Ilya Kostrikov · Justin Fu · Siddharth Reddy


Website: https://sites.google.com/view/icml-i3

Abstract: A key challenge for deploying interactive machine learning systems in the real world is the ability for machines to understand human intent. Techniques such as imitation learning and inverse reinforcement learning are popular data-driven paradigms for modeling agent intentions and controlling agent behaviors, and have been applied to domains ranging from robotics and autonomous driving to dialogue systems. Such techniques provide a practical solution to specifying objectives to machine learning systems when they are difficult to program by hand.

While significant progress has been made in these areas, most research effort has concentrated on modeling and controlling single agents from dense demonstrations or feedback. However, the real world has multiple agents, and dense expert data collection can be prohibitively expensive. Surmounting these obstacles requires progress in frontiers such as:
1) the ability to infer intent from multiple modes of data, such as language or observation, in addition to traditional demonstrations.
2) the ability to model multiple agents and their intentions, both in cooperative and adversarial settings.
3) handling partial or incomplete information from the expert, such as demonstrations that lack dense action annotations, raw videos, etc..

The workshop on Imitation, Intention, and Interaction (I3) seeks contributions at the interface of these frontiers, and will bring together researchers from multiple disciplines such as robotics, imitation and reinforcement learning, cognitive science, AI safety, and natural language understanding. Our aim will be to reexamine the assumptions in standard imitation learning problem statements (e.g., inverse reinforcement learning) and connect distinct application disciplines, such as robotics and NLP, with researchers developing core imitation learning algorithms. In this way, we hope to arrive at new problem formulations, new research directions, and the development of new connections across distinct disciplines that interact with imitation learning methods.

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Timezone: America/Los_Angeles


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