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Open Access

REVIEW

Machine Learning for Robotics: Algorithms, Applications, and Emerging Trends

Ahmed Ismail Ebada1,2, Yasmeen Abu-Seif2,*, Hrushikesh Pardeshi2,*, Nesma El-Sayed1
1 Information System Department, Faculty of Computers and Artificial Intelligence, Damietta University, Damietta, Egypt
2 HOPn Research Lab, Buchloe, Germany
* Corresponding Author: Yasmeen Abu-Seif. Email: email; Hrushikesh Pardeshi. Email: email
(This article belongs to the Special Issue: Machine Learning for Robotics: Algorithms, Applications, and Emerging Trends)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.081804

Received 12 March 2026; Accepted 20 May 2026; Published online 03 July 2026

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.

Keywords

Robot learning; sim-to-real; foundation models; human-robot interaction

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