Systematic Literature Review of Reinforcement Learning for Humanoid Robots

This Systematic Literature Review (SLR) investigates the state of the art of Reinforcement Learning (RL) applied to humanoid robots, exploring its main techniques, implementation challenges, comparisons with other control approaches, current limitations, possibilities of transfer learning, integration with other Artificial Intelligence (AI) techniques, and promising applications. The adopted methodology includes defining fundamental research questions, carefully selecting databases, constructing precise search strings, and rigorously applying inclusion criteria to ensure the relevance of the analyzed studies. The results highlight significant advances in the use of RL to optimize the performance of humanoid robots in complex tasks, with emphasis on techniques such as Deep Reinforcement Learning (DRL), Q-learning, and imitation learning. However, the research also reveals persistent challenges, such as the need to improve skill generalization, low sampling efficiency, high computational cost of training, and difficulties in adapting to dynamic environments. Additionally, the scarcity of standardized datasets for humanoid robotics and the reliance on simulations for training RL models hinder the transition to real-world applications. The study also identifies promising RL applications in humanoid robots, including home assistance, rehabilitation of patients with motor difficulties, exploration of hazardous environments, and AI-assisted social interactions. The ability of humanoid robots to learn autonomously and adapt to different contexts paves the way for more efficient, flexible systems that are less dependent on explicit programming. Furthermore, techniques such as transfer learning and imitation learning emerge as viable alternatives to mitigate some of the limitations faced by pure RL, reducing training time and allowing humanoid robots to reuse acquired knowledge in different tasks. The integration of RL with other approaches, such as convolutional neural networks for visual perception and supervised learning for policy refinement, also appears promising for enhancing the autonomy and intelligence of these systems. Thus, this review not only synthesizes the current state of research on RL for humanoid robots but also outlines directions for future investigations, emphasizing the need for more efficient approaches, more robust data, and hybrid strategies that combine different learning paradigms to overcome existing barriers.