A Kinect-Based Gesture Acquisition and Reproduction System for Humanoid Robots

Augello, Agnese; Ciulla, Angelo; Cuzzocrea, Alfredo; Gaglio, Salvatore; Pilato, Giovanni; Vella, Filippo

doi:10.1007/978-3-030-58799-4_69

Cited by 1 publication

(1 citation statement)

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“…Existing approaches to enhance Pepper's visual perception connected the robot's input and output functions with other visual perception libraries, such as the Robot Operating System. However, existing work has not integrated SOTA online human tracking and pose estimation, limiting its visual perception performance in complex scenarios [3,7].…”

Section: Introductionmentioning

confidence: 99%

Improving Visual Perception of a Social Robot for Controlled and In-the-wild Human-robot Interaction

Zhong,

Tian,

et al. 2024

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

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Social robots often rely on visual perception to understand their users and the environment. Recent advancements in data-driven approaches for computer vision have demonstrated great potentials for applying deep-learning models to enhance a social robot's visual perception. However, the high computational demands of deep-learning methods, as opposed to the more resource-efficient shallow-learning models, bring up important questions regarding their effects on real-world interaction and user experience. It is unclear how will the objective interaction performance and subjective user experience be influenced when a social robot adopts a deep-learning based visual perception model. We employed stateof-the-art human perception and tracking models to improve the visual perception function of the Pepper robot and conducted a controlled lab study and an in-the-wild human-robot interaction study to evaluate this novel perception function for following a specific user with other people present in the scene.

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Section: Introductionmentioning

confidence: 99%