5 papers
Efficiently Guiding Imitation Learning Agents with Human Gaze
Akanksha Saran, Ruohan Zhang, Elaine Schaertl Short +1
Human gaze is known to be an intention-revealing signal in human demonstrations of tasks. In this work, we use gaze cues from human demonstrators to enhance the performance of agen…
Leveraging Human Guidance for Deep Reinforcement Learning Tasks
Ruohan Zhang, Faraz Torabi, Lin Guan +2
Reinforcement learning agents can learn to solve sequential decision tasks by interacting with the environment. Human knowledge of how to solve these tasks can be incorporated usin…
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset
Ruohan Zhang, Calen Walshe, Zhuode Liu +6
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-qua…
An initial attempt of combining visual selective attention with deep reinforcement learning
Liu Yuezhang, Ruohan Zhang, Dana H. Ballard
Visual attention serves as a means of feature selection mechanism in the perceptual system. Motivated by Broadbent's leaky filter model of selective attention, we evaluate how such…
AGIL: Learning Attention from Human for Visuomotor Tasks
Ruohan Zhang, Zhuode Liu, Luxin Zhang +4
When intelligent agents learn visuomotor behaviors from human demonstrations, they may benefit from knowing where the human is allocating visual attention, which can be inferred fr…