Deep Reinforcement Learning for Bipedal Locomotion: A Brief Survey

TL;DR

This survey reviews deep reinforcement learning frameworks for bipedal robot locomotion, categorizing and analyzing their structures, strengths, and limitations to guide future research in creating versatile, real-world applicable solutions.

Contribution

It systematically categorizes and compares existing DRL frameworks for bipedal locomotion, highlighting research gaps and proposing future directions for integrated control schemes.

Findings

End-to-end frameworks vary in learning approaches.

Hierarchical frameworks combine learning and traditional methods.

Identified key limitations and future research directions.

Abstract

Bipedal robots are gaining global recognition due to their potential applications and advancements in artificial intelligence, particularly through Deep Reinforcement Learning (DRL). While DRL has significantly advanced bipedal locomotion, the development of a unified framework capable of handling a wide range of tasks remains an ongoing challenge. This survey systematically categorises, compares, and analyses existing DRL frameworks for bipedal locomotion, organising them into end-to-end and hierarchical control schemes. End-to-end frameworks are evaluated based on their learning approaches, while hierarchical frameworks are examined in terms of layered structures that integrate learning-based or traditional model-based methods. We provide a detailed evaluation of the composition, strengths, limitations, and capabilities of each framework. Additionally, this survey identifies key research gaps and proposes future directions aimed at creating a more integrated and efficient framework for bipedal locomotion, with wide-ranging applications in real-world environments.