Bioinspired Perception, Decision-Making and Adaptive Control for Embodied Robotic Systems

Dear Colleagues,

Embodied robotic systems integrate physical actuators, environmental perception, and cognitive decision-making to achieve adaptive interaction with complex real-world environments. Reliable physical interaction heavily depends on high-fidelity scene reconstruction, robust real-time localization, human-aware motion planning, natural human–robot interaction and collaboration supported by bioelectrical sensing. With the rapid progress of 3D reconstruction, deep learning, multi-sensor fusion, and brain–muscle electrical signal acquisition technologies, conventional SLAM and path planning frameworks still face prominent challenges in dynamic human-interactive scenarios, low-texture environments, and flexible human–robot co-working tasks. This Special Issue targets advanced SLAM, intelligent path planning, and bio-signal-driven human–robot interaction for embodied robotic systems, welcoming original innovative investigations and comprehensive review manuscripts. It unites cutting-edge mapping and localization theories, learning-based trajectory optimization, and neural interface (e.g., EEG/EMG)-enabled interactive control schemes applicable to ground mobile robots, UAVs, and embodied manipulation platforms. By bridging algorithm innovation and embedded deployment, this Special Issue addresses key bottlenecks in high-precision 3D modeling, stable pose tracking, dynamic obstacle avoidance, human intention recognition via bioelectrical signals, and safe interactive decision-making. It aims to promote the development of robust, lightweight, human-friendly embodied robotic systems that support accurate environmental perception, flexible autonomous movement, and seamless bio-signal-based human–robot collaboration in industrial, daily, and extreme working scenarios.

Topics of interest include, but are not limited to:

Advanced SLAM technologies: Visual simultaneous localization and mapping systems, LiDAR mapping and localization frameworks, multi-modal sensor fusion SLAM strategies, and real-time mapping and localization paradigms based on 3D Gaussian Splatting technology for embodied agents.

Robust perception and mapping: Dynamic human-scene adaptive SLAM algorithms, environmental perception and mapping optimization for low-texture and low-illumination scenarios, accurate loop closure detection mechanisms, and global and local map refinement and updating algorithms suited for physical interaction tasks.

Human-aware intelligent path planning algorithms: Human-friendly global optimal path searching strategies, real-time dynamic human obstacle avoidance methods, human motion prediction and trajectory optimization schemes, and data-driven intelligent planning frameworks embedded with human intention perception.

Multi-embodied robot collaboration technologies: Synchronous collaborative SLAM mechanisms for embodied robot clusters, multi-agent swarm intelligence and cooperative control, distributed cooperative path planning strategies, multi-agent task allocation and trajectory conflict elimination methods for group embodied systems.

Bioelectrical human–robot interaction and physical collaborative control: Human intention decoding based on EEG, EMG, etc., bio-signal-driven interactive interfaces, human-in-the-loop shared control, and safety constraint mechanisms for human-embodied robot coexistence environments.

Engineering application domains: Interactive autonomous navigation for ground embodied robots, human-coordinated trajectory planning for embodied UAV platforms, and physical human–robot cooperative manipulation strategies for industrial and domestic embodied systems.

Edge deployment and lightweight optimization: Lightweight compression of SLAM reconstruction models, acceleration optimization of planning and bio-signal interaction algorithms, and embedded real-time deployment verification for resource-limited embodied robotic hardware.

Dr. Dongxu Gao
Dr. Qing Gao
Dr. Tong Cui
Dr. Kairu Li
Guest Editors

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