홈 › Papers

A Framework for Studying Reinforcement Learning and Sim-to-Real in Robot Soccer

2020-08-18 · Hansenclever F. Bassani, Renie A. Delgado, José Nilton de O. Lima Junior, Heitor R. Medeiros, Pedro H. M. Braga, Mateus G. Machado, Lucas H. C. Santos, Alain Tapp

This article introduces an open framework, called VSSS-RL, for studying Reinforcement Learning (RL) and sim-to-real in robot soccer, focusing on the IEEE Very Small Size Soccer (VSSS) league. We propose a simulated environment in which continuous or discrete control policies can be trained to control the complete behavior of soccer agents and a sim-to-real method based on domain adaptation to adapt the obtained policies to real robots. Our results show that the trained policies learned a broad repertoire of behaviors that are difficult to implement with handcrafted control policies. With VSSS-RL, we were able to beat human-designed policies in the 2019 Latin American Robotics Competition (LARC), achieving 4th place out of 21 teams, being the first to apply Reinforcement Learning (RL) successfully in this competition. Both environment and hardware specifications are available open-source to allow reproducibility of our results and further studies.

📄 PDF Abstract BibTeX arXiv:2008.12624

Code (0)

등록된 구현이 없습니다.

Tasks

Domain Adaptation reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Methods 이 논문이 사용한 방법론

American 설명 없음

rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot Soccer

2021-06-15 · Felipe B. Martins, Mateus G. Machado, Hansenclever F. Bassani, Pedro H. M. Braga 외

Reinforcement learning is an active research area with a vast number of applications in robotics, and the RoboCup competition is an interesting environment for studying and evaluating reinforcement learning methods. A kn…

OpenAI Gym reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot

2022-08-01 · Yandong Ji, Zhongyu Li, Yinan Sun, Xue Bin Peng 외

We address the problem of enabling quadrupedal robots to perform precise shooting skills in the real world using reinforcement learning. Developing algorithms to enable a legged robot to shoot a soccer ball to a given ta…

Deep Reinforcement Learning Friction Hierarchical Reinforcement Learning Motion Planning+3

Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams

2025-05-20 · Zhi Su, Yuman Gao, Emily Lukas, Yunfei Li 외

Achieving coordinated teamwork among legged robots requires both fine-grained locomotion control and long-horizon strategic decision-making. Robot soccer offers a compelling testbed for this challenge, combining dynamic,…

Multi-agent Reinforcement Learning

HierKick: Hierarchical Reinforcement Learning for Vision-Guided Soccer Robot Control

2026-03-01 · Yizhi Chen, Zheng Zhang, Zhanxiang Cao, Yihe Chen 외 arxiv

Controlling soccer robots involves multi-time-scale decision-making, which requires balancing long-term tactical planning and short-term motion execution. Traditional end-to-end reinforcement learning (RL) methods face c…

Hierarchical Reinforcement Learning

DribbleBot: Dynamic Legged Manipulation in the Wild

2023-04-03 · Yandong Ji, Gabriel B. Margolis, Pulkit Agrawal

DribbleBot (Dexterous Ball Manipulation with a Legged Robot) is a legged robotic system that can dribble a soccer ball under the same real-world conditions as humans (i.e., in-the-wild). We adopt the paradigm of training…

reinforcement-learning