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Toward Embodied Intelligence: State-of-the-Art in Sensing, Decision-Making and Control Technologies for Autonomous Robots (2nd Edition)

This special issue belongs to the section “Sensors and Robotics“.

Special Issue Information

Dear Colleagues,

With the advantages of noteworthy dexterity, maneuverability, and high efficiency in performing a growing variety of tasks, autonomous robots are becoming increasingly intelligent and complex with the ability to complete difficult operations with the comprehensive utilization of multimodal sensors, embodied intelligence frameworks, and large model-driven controllers. Autonomous robots can act in swarms with cognitive reasoning and offering total flexibility for industrial applications, which have expanded significantly from manufacturing and automation to healthcare, agriculture, and human–robot collaboration. This Special Issue aims to provide up-to-date research concepts, theoretical findings, and practical solutions for autonomous robots in relation to AI-augmented perception, embodied decision-making, and adaptive motion control, with a focus on sensorimotor coordination, human–robot value alignment, and safety-critical autonomy. We invite contributions that bridge theoretical advances and practical deployments, aiming to redefine the boundaries of autonomous robotics in the era of embodied intelligence.

Dr. Yuanlong Xie
Prof. Dr. Shuting Wang
Prof. Dr. Shane Xie
Guest Editors

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Keywords

  • kinematic and dynamic modelling and parameter identification
  • learning-based perception, recognition, navigation, mapping, and localization
  • generative AI-driven perception and multimodal sensor fusion
  • understanding of intelligent decisions, cooperation, environments, and situations
  • AI-driven multi-agent decision-making (e.g., reinforcement learning, meta-learning)
  • large language model (LLM)-guided task planning and cognitive reasoning
  • foundation models for embodied intelligence and robot-environment interaction
  • mobile robot manipulation and in-wheel-driven techniques
  • cooperative control of multiple autonomous systems
  • coexisting–cooperative–cognitive technologies
  • autonomous levels of unmanned systems
  • robot optimal control, adaptive control, and system optimization
  • model-based and model-free reinforcement learning for adaptive control

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