EgoSpot:Egocentric Multimodal Control for Hands-Free Mobile Manipulation
Ganlin Zhang, Deheng Zhang, Longteng Duan, Guo Han, Yuqian Fu, Danda Pani Paudel, Luc Van Gool, Eric Vollenweider

TL;DR
This paper introduces EgoSpot, a multimodal, hands-free control system for the Boston Dynamics Spot robot using egocentric signals like gaze, gestures, and voice, enhancing accessibility for users with disabilities.
Contribution
It presents a novel egocentric multimodal control framework integrating gaze, gestures, and voice for robot manipulation, improving accessibility and natural interaction.
Findings
Performance comparable to joystick control in task completion.
Significant improvement in accessibility and naturalness of interaction.
Potential to make mobile manipulation robots more inclusive.
Abstract
We propose a novel hands-free control framework for the Boston Dynamics Spot robot using the Microsoft HoloLens 2 mixed-reality headset. Enabling accessible robot control is critical for allowing individuals with physical disabilities to benefit from robotic assistance in daily activities, teleoperation, and remote interaction tasks. However, most existing robot control interfaces rely on manual input devices such as joysticks or handheld controllers, which can be difficult or impossible for users with limited motor capabilities. To address this limitation, we develop an intuitive multimodal control system that leverages egocentric sensing from a wearable device. Our system integrates multiple control signals, including eye gaze, head gestures, and voice commands, to enable hands-free interaction. These signals are fused to support real-time control of both robot locomotion and arm…
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Taxonomy
TopicsGaze Tracking and Assistive Technology · Teleoperation and Haptic Systems · Virtual Reality Applications and Impacts
