@article{Ebada2026, 
author = {Ahmed Ismail Ebada and Yasmeen Abu-Seif and Hrushikesh Pardeshi and Nesma El-Sayed},
title = {Machine Learning for Robotics: Algorithms, Applications, and Emerging Trends},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {88},
number = {3},
pages = {2},
keywords = {Robot learning, sim-to-real, foundation models, human-robot interaction},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.081804},
doi = {10.32604/cmc.2026.081804},
abstract = {The integration of Deep Learning, Deep Reinforcement Learning, and massive Vision-Language-Action (VLA) foundation models has catalysed a profound paradigm shift in robotics, transitioning systems from rigid automation to dynamic, open-world autonomy. Despite transformative breakthroughs in fields such as healthcare, ranging from adaptive robotic rehabilitation to autonomous surgical manipulation and silver care, widespread real-world deployment remains severely bottlenecked. This limitation primarily stems from the “Reality Gap” inherent to sim-to-real transfer and a fundamental epistemological tension: the stochastic, “black-box” nature of unconstrained neural networks fundamentally conflicts with the deterministic, zero-violation safety guarantees demanded by physical robotics. To address these critical barriers, this comprehensive review systematically synthesises state-of-the-art algorithmic building blocks across perception, dynamics modelling, and control. Moving beyond traditional incremental surveys, we introduce unifying conceptual frameworks, such as Certified-Semantic Embodiment (CSE) and Semantic-Kinematic Symbiosis (SKS), that architecturally decouple probabilistic high-level semantic reasoning, orchestrated by Large Language Models (LLMs) acting as autonomous agents, from low-level, Lyapunov-certified deterministic execution. Furthermore, we formalise the evaluation pipeline for deployment realities, recommending a shift from empirical success rates to mathematically bounded frameworks such as Prediction-Powered Inference (PPI) to ensure robust sim-to-real generalisation. Ultimately, this review provides a rigorous technical roadmap for bridging the semantic-kinematic divide. By integrating cognitive adaptability with rigorous physical constraints, we aim to ensure that the next generation of embodied AI achieves human-level intelligence while strictly meeting the safety, accountability, and regulatory requirements for dependable clinical and industrial deployment.}
}