Version 1
: Received: 10 July 2024 / Approved: 10 July 2024 / Online: 10 July 2024 (14:00:13 CEST)
How to cite:
Idowu, E. Bridging the Embodiment Gap: Embodied AI for Enhanced Human-Machine Collaboration and Learning in Dynamic Environments. Preprints2024, 2024070869. https://doi.org/10.20944/preprints202407.0869.v1
Idowu, E. Bridging the Embodiment Gap: Embodied AI for Enhanced Human-Machine Collaboration and Learning in Dynamic Environments. Preprints 2024, 2024070869. https://doi.org/10.20944/preprints202407.0869.v1
Idowu, E. Bridging the Embodiment Gap: Embodied AI for Enhanced Human-Machine Collaboration and Learning in Dynamic Environments. Preprints2024, 2024070869. https://doi.org/10.20944/preprints202407.0869.v1
APA Style
Idowu, E. (2024). Bridging the Embodiment Gap: Embodied AI for Enhanced Human-Machine Collaboration and Learning in Dynamic Environments. Preprints. https://doi.org/10.20944/preprints202407.0869.v1
Chicago/Turabian Style
Idowu, E. 2024 "Bridging the Embodiment Gap: Embodied AI for Enhanced Human-Machine Collaboration and Learning in Dynamic Environments" Preprints. https://doi.org/10.20944/preprints202407.0869.v1
Abstract
This study explores the limitations of current AI systems, which predominantly function in the digital realm, and investigates the transformative potential of embodied AI. Embodied AI involves AI agents equipped with physical bodies, enabling them to interact directly with the physical world. This research focuses on how embodied AI can bridge the "embodiment gap" and enhance human-machine collaboration and learning in dynamic environments. The research examines the fundamental differences between traditional AI systems and embodied AI, emphasizing the importance of physical interaction for contextual understanding, adaptive learning, and intuitive human-machine collaboration. It explores various applications of embodied AI, including robotics, autonomous vehicles, and assistive technologies, demonstrating how physical embodiment can improve performance, safety, and user experience. Through experimental studies and real-world case analyses, the study highlights the advantages of embodied AI in tasks requiring situational awareness, dexterity, and real-time decision-making. It also addresses the challenges associated with developing and deploying embodied AI systems, such as sensor integration, real-time processing, and human-machine interface design. Findings indicate that embodied AI can significantly improve the efficacy of AI systems in dynamic and unpredictable environments. By leveraging physical embodiment, AI agents can better understand and respond to their surroundings, facilitating more natural and effective interactions with humans. The research concludes with recommendations for advancing embodied AI, including interdisciplinary collaboration, investment in sensor and actuator technologies, and the development of standardized frameworks for embodied intelligence.
Keywords
embodied AI, human-machine collaboration, dynamic environments, robotics, physical interaction, adaptive learning, situational awareness, real-time decision-making, human-computer interaction, AI in the physical world.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
