Human gaze reveals a wealth of information about internal cognitive state. Thus, gaze-
related research has significantly increased in computer vision, natural language …

A longstanding goal of artificial intelligence is to create artificial agents capable of learning
to perform tasks that require sequential decision making. Importantly, while it is the artificial …

Safe and efficient crowd navigation for mobile robot is a crucial yet challenging task.
Previous work has shown the power of deep reinforcement learning frameworks to train …

This paper describes a feature extraction and gaze estimation software, named\textit {Pistol}
that can be used with Pupil Invisible projects and other eye trackers in the future. In offline …

Humans race drones faster than neural networks trained for end-to-end autonomous flight.
This may be related to the ability of human pilots to select task-relevant visual information …

Large-scale public datasets have been shown to benefit research in multiple areas of
modern artificial intelligence. For decision-making research that requires human data, high …

Inspired by human visual attention, we propose a novel inverse reinforcement learning
formulation using Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) for …

Generative design problems often encompass complex action spaces that may be divergent
over time, contain state-dependent constraints, or involve hybrid (discrete and continuous) …

In this paper, we present a new approach for decomposing scan paths and its utility for
generating new scan paths. For this purpose, we use the K-Means clustering procedure to …

Pedestrian trajectory prediction is a critical yet challenging task especially for crowded
scenes. We suggest that introducing an attention mechanism to infer the importance of …