Gaze-Informed Multi-Objective Imitation Learning from Human Demonstrations

Abstract

In the field of human-robot interaction, teaching learning agents from human demonstrations via supervised learning has been widely studied and successfully applied to multiple domains such as self-driving cars and robot manipulation. However, the majority of the work on learning from human demonstrations utilizes only behavioral information from the demonstrator, i.e. what actions were taken, and ignores other useful information. In particular, eye gaze information can give valuable insight towards where the demonstrator is allocating their visual attention, and leveraging such information has the potential to improve agent performance. Previous approaches have only studied the utilization of attention in simple, synchronous environments, limiting their applicability to real-world domains. This work proposes a novel imitation learning architecture to learn concurrently from human action demonstration and eye tracking data to solve tasks where human gaze information provides important context. The proposed method is applied to a visual navigation task, in which an unmanned quadrotor is trained to search for and navigate to a target vehicle in a real-world, photorealistic simulated environment. When compared to a baseline imitation learning architecture, results show that the proposed gaze augmented imitation learning model is able to learn policies that achieve significantly higher task completion rates, with more efficient paths, while simultaneously learning to predict human visual attention. This research aims to highlight the importance of multimodal learning of visual attention information from additional human input modalities and encourages the community to adopt them when training agents from human demonstrations to perform visuomotor tasks.

Gaze-Guided Class Activation Mapping: Leveraging Human Attention for Network Attention in Chest X-rays Classification

“…By doing so, the network can further process the information conveyed in the visual heat maps to enhance performance; however, this adds an input to the network, making it harder to deploy to real-world tasks. The other approach avoids the dependency on the visual heat maps through representation learning, by using the visual heat maps to facilitate the learning of representative and robust features through transfer learning [8,9,41] or multi-task learning [2]. Still, transfer learning methods may suffer from negative transfer or overfitting, and multi-task learning methods commonly introduce a large number additional parameters to the network.…”