Robots Autonomously Detecting People: A Multimodal Deep Contrastive Learning Method Robust to Intraclass Variations

Fung, Angus; Benhabib, B.; Nejat, Goldie

doi:10.1109/lra.2023.3269306

Cited by 8 publications

(1 citation statement)

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“…These representations serve as the foundation for the robot to comprehend and achieve its intended purpose. Secondly, machine learning should encompass the calibration of sensors and actuators [36][37][38][39][40][41][42]. This calibration process enhances the precision and accuracy of these critical components, enabling the robot to interact more effectively with its environment.…”

Section: Machine Learning In Roboticsmentioning

confidence: 99%

Elevating Mobile Robotics: Pioneering Applications of Artificial Intelligence and Machine Learning

Nasrallah,

Stepanyan,

Nassrullah

et al. 2024

RIA

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The present study delves into the utilization of subsumption architecture for the modeling of mobile robot behaviors, particularly those that respond adaptively to environmental dynamics and inaccuracies in sensor measurements. Central to this investigation is the deployment of reactive controller networks, wherein each node-representing a distinct state-is governed by sensor-triggered conditions that dictate state transitions. The methodology adopted comprises a thorough literature review, encompassing sources from IEEE Xplore, ScienceDirect, and the ACM Digital Library, which discuss the integration of subsumption architecture in the realm of mobile robot control. Through this review, the effectiveness of subsumption architecture in crafting reactive robotic behaviors is underscored. It has been established that augmented finite state machines (AFSMs), which are integral to the subsumption architecture and possess internal timing mechanisms, are pivotal in managing the temporal aspects of state transitions. Additionally, the technique of layering-merging multiple simple networks to form intricate behavior patterns-emerges as a significant finding, accentuating the architecture's capability to facilitate complex behavioral constructs. The prime contribution of this body of work lies in identifying and elucidating the strategic role of subsumption architecture in enhancing the adaptability and robustness of mobile robots. The insights gleaned from this study not only advance our understanding of robotic control systems but also hold implications for the amplification of industrial efficiency and effectiveness through the application of sophisticated AI and machine learning techniques in mobile robotics.

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