This review aims to clarify the development path of DRL-driven robot adaptive control, offering reference for theory, algorithm design, and engineering practice and it is hoped that this survey will help new researchers quickly grasp the landscape of the field.
This paper presents the development and implementation of an end-to-end control framework for a quadruped walking robot based on deep reinforcement learning. The primary objective of the study is to design and verify a control system capable of autonomously generating locomotion strategies. A model of the walking robot...
Filip Połatyński, Paweł Skruch· International Conference on...· 0 citations
Feature-fusion Multi-Agent Proximal Policy Optimization (FMAPPO) is proposed, which fuses 2D LiDAR measurements with task-specific state information to enable safe decentralized multi-robot task assignment and navigation and demonstrates that the learned decentralized policies can coordinate multiple robots to service...
A. Abdalwhab, Giovanni Beltrame, David St-Onge· 0 citations
This survey examines RL-based AD in modular and end-to-end pipelines and relates reported methods to task formulation and deployment evidence and examines deployment barriers, including safety, Sim2Real generalization, data efficiency, computation, embodied alignment, and evaluation readiness.
B. Shuai, Min Hua, Le-Tian Tao et al.· Communications in Transporta...· 0 citations
This paper introduces a novel real-world RL algorithm that allows autonomous vehicles to collect informative scenarios and learn from them with minimal human intervention, and considers the learning progress of autonomous vehicles to identify informative scenarios and abort episodes before they enter unsafe states.
Daehyeok Kwon, Seung-Woo Seo, Sang-Hyun Lee· Proceedings of the Thirty-Fi...· 0 citations
A training method for HIL online reinforcement learning for real robots that automatically switches between learning from interventions and on-policy self-improvement, reducing the policy--target-sample gap that otherwise induces execution-time distribution shift.
Zihang Wang, Yishan Wang· 1 citation
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