Intelligent Robotics: Integrating Perception, Learning and Autonomous Control
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".
Deadline for manuscript submissions: 20 November 2026 | Viewed by 214
Special Issue Editor
Interests: biologically inspired robotics; mobile robots; artificial life; evolutionary computation; neuroethology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The theme of intelligent robotics focuses on integrating Artificial Intelligence (AI) with physical robotic systems to design machines capable of autonomous decision-making, adaptive control, and meaningful interactions with the real world. The field encompasses robot learning, human–robot interactions, and physical AI supported by multimodal sensory inputs, including computer vision and audio and tactile perception. Multidisciplinary research efforts and investigations into fundamental challenges in intelligent robotics are strongly encouraged.
Recent advances in large-scale foundation models and embodied AI have profoundly influenced the field of intelligent robotics. Although foundation models have demonstrated remarkable generalization capabilities in the language and vision domains, their deployment in physical robotic systems remains a significant challenge. Enabling autonomous robots to perform real-world reasoning, adapt to dynamic environments, and execute long-horizon tasks represents an emerging and critical research frontier.
This Special Issue aims to explore not only the fundamental mechanisms through which perception and action shape robotic behavior but also how large-scale models, multimodal learning, adaptation, and physical interaction capabilities can empower robots to exhibit increasingly intelligent and autonomous behaviors.
Topics of interest include, but are not limited to, the following:
- Physical AI;
- Robot learning;
- Object recognition and manipulation;
- Spatial navigation;
- Field robotics;
- Sensor fusion;
- Human robot interaction;
- Social robotics and collaborative robotics;
- Manufacturing robots;
- Industrial and service robot applications;
- Robot control;
- Imitation learning;
- Reinforcement learning in robotics;
- Biologically inspired robots;
- Active perception;
- Multimodal sensor integration;
- Physical interaction intelligence;
- Real-world reasoning and planning;
- Vision-language-action models;
- Foundation models for robotics.
Prof. Dr. DaeEun Kim
Guest Editor
Manuscript Submission Information
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Keywords
- robot learning
- perception integration
- robot control
- physical AI
- physical interaction intelligence
- multimodal sensor integration
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