Reinforcement Learning For Quadrupedal Locomotion: Current Advancements And Future Perspectives
Maurya Gurram, Prakash Kumar Uttam, Shantipal S. Ohol
TL;DR
This paper surveys reinforcement learning approaches to quadrupedal locomotion, addressing how policies are learned, adapted, and transferred from simulation to real hardware across diverse terrains. It covers algorithmic families (e.g., PPO, TRPO), learning curricula, reward shaping, and methods for bridging the sim-to-real gap, including privileged learning and teacher-student frameworks. Key contributions include outlining adaptation strategies like Rapid Motor Adaptation, domain randomization techniques, and exteroceptive sensing concepts that can inform future controller design. The work provides a practical roadmap for researchers and practitioners to develop more robust, transferable, and capable quadrupedal locomotion systems for real-world environments.
Abstract
In recent years, reinforcement learning (RL) based quadrupedal locomotion control has emerged as an extensively researched field, driven by the potential advantages of autonomous learning and adaptation compared to traditional control methods. This paper provides a comprehensive study of the latest research in applying RL techniques to develop locomotion controllers for quadrupedal robots. We present a detailed overview of the core concepts, methodologies, and key advancements in RL-based locomotion controllers, including learning algorithms, training curricula, reward formulations, and simulation-to-real transfer techniques. The study covers both gait-bound and gait-free approaches, highlighting their respective strengths and limitations. Additionally, we discuss the integration of these controllers with robotic hardware and the role of sensor feedback in enabling adaptive behavior. The paper also outlines future research directions, such as incorporating exteroceptive sensing, combining model-based and model-free techniques, and developing online learning capabilities. Our study aims to provide researchers and practitioners with a comprehensive understanding of the state-of-the-art in RL-based locomotion controllers, enabling them to build upon existing work and explore novel solutions for enhancing the mobility and adaptability of quadrupedal robots in real-world environments.