David Hinwood

In this paper, we present a novel framework for robotic attention modulation that enables dynamic regulation between attention-seeking and attention-avoidance behaviours through gaze feedback. For this feedback, we incorporate SGE with gaze fixation counts to create a metric that evaluates not just the quantity but also the quality of visual attention. Our approach implements two complementary modules, a Gaze Avoidance Module (GAM) and a Gaze Garnering Module (GGM), both powered by reinforcement learning (RL) algorithms that respond to real-time human gaze patterns. Thus the system continuously adapts to each individual’s current gaze feedback level, dynamically calibrating the robot’s attention-modulating behaviours to match personal attention thresholds. This personalisation ensures that the robot can effectively engage with users when needed, while remaining unobtrusive when appropriate, all based on real-time analysis of human gaze patterns.

This paper presents the design of a human-inspired gripper for textile manipulation, drawing on insights from human hand biomechanics and textile handling techniques to create a gripper that can effectively manipulate textiles in waste sorting applications.

Researching human-robot interaction “in the wild” can sometimes require insight from different fields. Experiments that involve collaborative tasks are valuable opportunities for studying HRI and developing new tools. The following describes a framework for an “in the wild” experiment situated in a public museum that involved a Wizard of OZ (WOZ) controlled robot. The UR10 is a non-humanoid collaborative robot arm and was programmed to engage in a collaborative drawing task. The purpose of this study was to evaluate how movement by a non-humanoid robot could affect participant experience. While the current framework is designed for this particular task, the control architecture could be built upon to provide a base for various collaborative studies.