Research Article
Incremental Learning Technology for Embodied Evolution of Intelligent Robots
@INPROCEEDINGS{10.4108/eai.22-5-2026.2365329, author={Chunxi Yu}, title={Incremental Learning Technology for Embodied Evolution of Intelligent Robots}, proceedings={Proceedings of the 4th International Conference on Image, Algorithms, and Artificial Intelligence, ICIAAI 2026, 22-24 May 2026, Singapore, Singapore}, publisher={EAI}, proceedings_a={ICIAAI}, year={2026}, month={8}, keywords={Incremental Learning Intelligent Robots Embodied Evolution}, doi={10.4108/eai.22-5-2026.2365329} }- Chunxi Yu
Year: 2026
Incremental Learning Technology for Embodied Evolution of Intelligent Robots
ICIAAI
EAI
DOI: 10.4108/eai.22-5-2026.2365329
Abstract
With the progression of intelligent robots from controlled laboratory environments to complex and unbound real-world environments. The need for the robots to adapt to the changing environment has become of critical importance. In this study, the incremental learning methodologies are explored as the basis for achieving embodied evolution in robots. This study explores the three main incremental learning (IL) methodologies, including parameter regularization, knowledge distillation and structural expansion. The study also explores the new paradigm shift of semantic evolution using large language models and the unified world modeling approach. The study is of vital importance for the development of the next generation of autonomous robots.
