Reinforcement Learning Applications in Autonomous Robotics

Abstract

Reinforcement Learning (RL) has emerged as a powerful paradigm for enabling autonomous robots to learn robust control and decision-making policies from interaction with their environments. Unlike classical control, RL optimizes long-horizon objectives under uncertainty, allowing robots to adapt to variable dynamics, partial observability, and noisy sensors. This article surveys core RL approaches for robotics—including value-based, policy-gradient, actor–critic, model-based, and hybrid methods—and maps them to canonical tasks such as mobile navigation, manipulation, locomotion, and aerial autonomy. We discuss sample-efficiency challenges, safety-constrained exploration, sim-to-real transfer, multi-robot coordination, and human-in-the-loop learning. We present system design patterns spanning perception, state estimation, control stacks, and MLOps for reliable deployment. Finally, we outline evaluation practices and open problems, emphasizing safety certification, uncertainty quantification, energy awareness, and trustworthy decision-making in safety-critical domains