A connectionist model of associating proprioceptive and tactile modalities in a humanoid robot

Malinovská, Kristína; Farkaš, Igor; Harvanova, Jana; Hoffmann, Matej

doi:10.1109/icdl53763.2022.9962195

Cited by 2 publications

(1 citation statement)

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“…This information can be used to control the robot's movements, to detect and diagnose failures, and to plan its actions. For example, Malinovská et al ( 2022 ) have developed a neural network model that can learn proprioceptive-tactile representations on a simulated humanoid robot, demonstrating the ability to accurately predict touch and its location from proprioceptive information. However, further work is needed to address the model's limitations.…”

Section: Modalities Used In Human–robot Interactionmentioning

confidence: 99%

Recent advancements in multimodal human–robot interaction

Yang

et al. 2023

Front. Neurorobot.

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Robotics have advanced significantly over the years, and human–robot interaction (HRI) is now playing an important role in delivering the best user experience, cutting down on laborious tasks, and raising public acceptance of robots. New HRI approaches are necessary to promote the evolution of robots, with a more natural and flexible interaction manner clearly the most crucial. As a newly emerging approach to HRI, multimodal HRI is a method for individuals to communicate with a robot using various modalities, including voice, image, text, eye movement, and touch, as well as bio-signals like EEG and ECG. It is a broad field closely related to cognitive science, ergonomics, multimedia technology, and virtual reality, with numerous applications springing up each year. However, little research has been done to summarize the current development and future trend of HRI. To this end, this paper systematically reviews the state of the art of multimodal HRI on its applications by summing up the latest research articles relevant to this field. Moreover, the research development in terms of the input signal and the output signal is also covered in this manuscript.

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Section: Modalities Used In Human–robot Interactionmentioning

confidence: 99%