Embodied Intelligence: Continuous Learning Algorithms and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 13
Special Issue Editor
Special Issue Information
Dear Colleagues,
This Special Issue investigates the use of continuous learning algorithms as the foundation for advancing embodied intelligence in AI and robotics. It focuses on how systems with a physical form can continue to adapt to dynamic environments and tasks without catastrophic ability forgetting. Key areas within which continuous learning is applied include the following:
- Deep Learning Architectures: Novel neural models designed specifically for the lifelong acquisition and retention of knowledge in embodied agents.
- Robotic Adaptation: Reinforcement and evolutionary learning methods enabling robots to continuously refine their skills (e.g., manipulation, navigation) through interaction and experience.
- Vision and Language Integration: Continuous learning approaches to enable agents to improve their multimodal understanding (e.g., scene comprehension, instruction following) in real time.
- AI Frameworks: Theoretical and algorithmic advances (e.g., generative replay, regularization, Bayesian methods) tackling core continual learning challenges like stability and plasticity.
This Special Issue seeks contributions on algorithms, architectures, and applications where continuous learning enables embodied systems to grow, adapt, and operate effectively in complex, evolving real-world settings.
Dr. Huaidong Zhang
Guest Editor
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Keywords
- deep learning
- robotics
- vision and language
- artificial intelligence
- continuous learning
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