Using Human Gaze to Improve Robustness against Irrelevant Objects in Robot Manipulation Tasks

Abstract

Deep imitation learning enables the learning of complex visuomotor skills from raw pixel inputs. However, this approach suffers from the problem of overfitting to the training images. The neural network can easily be distracted by task-irrelevant objects. In this letter, we use the human gaze measured by a head-mounted eye tracking device to discard task-irrelevant visual distractions. We propose a mixture density network-based behavior cloning method that learns to imitate the human gaze. The model predicts gaze positions from raw pixel images and crops images around the predicted gazes. Only these cropped images are used to compute the output action. This cropping procedure can remove visual distractions because the gaze is rarely fixated on task-irrelevant objects. This robustness against irrelevant objects can improve the manipulation performance of robots in scenarios where task-irrelevant objects are present. We evaluated our model on four manipulation tasks designed to test the robustness of the model to irrelevant objects. The results indicate that the proposed model can predict the locations of task-relevant objects from gaze positions, is robust to task-irrelevant objects, and exhibits impressive manipulation performance especially in multi-object handling.