Deep Reinforcement Learning for Bipedal Locomotion: A Brief Survey

2024-04-25

arXiv.org

AI Summary

Existing deep reinforcement learning (DRL) frameworks for bipedal robot multi-task motion control suffer from poor generalization, low sim-to-real transfer efficiency, and a lack of systematic benchmarking. Method: We propose the first unified DRL taxonomy tailored to bipedal control, rigorously delineating the trade-offs between end-to-end and hierarchical control across task coverage, policy interpretability, and sim-to-real transfer. Our integrated framework synergizes PPO/SAC, hierarchical RL, model predictive control (MPC), and neural policy representation, accompanied by deployment principles balancing robustness and scalability. Contribution/Results: Leveraging a structured evaluation matrix spanning 30+ state-of-the-art methods, we identify multi-task generalization and cross-domain transfer as the two fundamental bottlenecks. This work establishes both theoretical foundations and engineering guidelines for DRL-driven embodied intelligent control.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Develop unified DRL framework
• Evaluate bipedal locomotion frameworks
• Identify research gaps in DRL

Innovation

• Deep Reinforcement Learning techniques
• End-to-end control frameworks
• Hierarchical layered control structures