Humanoid Robot Kick in Motion Ability for Playing Robotic Soccer

Teixeira, Henrique; Silva, Tiago; Abreu, Miguel; Reis, Luís Paulo

doi:10.1109/icarsc49921.2020.9096073

Cited by 19 publications

(7 citation statements)

References 24 publications

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“…In contrast, human kicking skills are performed continuously while running. Abreu et al [100,101] used the PPO algorithm to enable robots to learn the kicking skill while moving from scratch, just like humans, without the need to adjust the kicking point and waste time. In this study, the robot first uses its existing walking skills to walk to a kicking point close to the ball.…”

Section: Reinforcement Learning Methodsmentioning

confidence: 99%

A survey of research on several problems in the RoboCup3D simulation environment

Gao,

Yi,

Jin

et al. 2024

Auton Agent Multi-Agent Syst

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In the process of robot research and development, due to the vulnerability of hardware, simulation environment is often used to verify and test algorithms first. RoboCup3D simulation environment is developed based on open dynamic engine, and the humanoid robot NAO is modeled as the main robot, which provides a simulation platform for humanoid robot researchers to study robot movements. At the same time, it is also the official platform of RoboCup 3D events. Under the rules of soccer robot competition, it is helpful for the research of multi-robots, especially multi-humanoid robots' cooperation strategy. This paper summarizes the related research in RoboCup3D simulation environment, and first introduces the basic problems existing in this simulation environment. Secondly, the research of robot motion generation and optimization based on model and nonmodel in simulation environment is introduced respectively. Then, it introduces the related research of cooperation strategy design of multi-humanoid robots under RoboCup3D rules, including positioning, dynamic role assignment, etc. And sort out a typical practical solution to the above problems; Finally, the future development trend of related research in RoboCup3D simulation environment is analyzed.

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Section: Reinforcement Learning Methodsmentioning

confidence: 99%

A survey of research on several problems in the RoboCup3D simulation environment

Gao,

Yi,

Jin

et al. 2024

Auton Agent Multi-Agent Syst

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“…We can find works that use reinforcement learning to learn the best transition in a state machine that represents a setplay [Fabro et al 2014], or individual decision-making by robot soccer players [Shi et al 2018]. Some works investigate the transfer of knowledge from the simulated environment to real robots [Bianchi et al 2018].It is also worth mentioning the presence of many works that use deep reinforcement learning to train skills in soccer robots, such as walking, running, kicking [Abreu et al 2019], [Melo et al 2021], [Abreu et al 2021], [Spitznagel et al 2021], [Teixeira et al 2020].…”

Section: Related Workmentioning

confidence: 99%

Learning by Demonstration of Coordinated Plans in Multiagent Systems

Simões¹,

Nogueira²

2022

Anais Estendidos Do XIV Simpósio Brasileiro De Robótica E XIX Simpósio Latino-Americano De Robótica (SBR/LARS Estendido 2022)

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One of the significant challenges in Multiagent Systems (MAS) is the creation of cooperative plans to deal with the different scenarios that present themselves in a dynamic, real-time environment composed of teams of mobile robots. This work involves capturing human knowledge to demonstrate how robot teams can better cooperate in solving the problem they must solve. The research used the environment RoboCup 3D Soccer Simulation (3DSSIM) and the collection of human demonstrations were carried out through a set of tools developed from adapting existing solutions in the RoboCup community using a crowdsourcing strategy. In addition, fuzzy clustering was used to gather human demonstrations (setplays) with the same semantic meaning, even with minor differences. With the data organized, this work used a reinforcement learning mechanism to learn a classification policy that allows agents to decide which group of setplays is best suited to each situation that presents itself in the environment. The results show the ability of the robot team to evolve, from learning the suggested setplays and their use in an appropriate way to the skills of each robot.

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“…Our learning framework employs the Proximal Policy Optimization (PPO) algorithm, introduced by Schulman et al [37], which was chosen due to its success in optimizing low-level skills concerning the NAO robot [21,38,39,40,41], and highlevel skills [42], where it outperformed other algorithms such as TRPO or DDPG. The chosen implementation uses the clipped surrogate objective:…”

Section: Learning Frameworkmentioning

confidence: 99%

Robust Biped Locomotion Using Deep Reinforcement Learning on Top of an Analytical Control Approach

Kasaei,

Abreu,

Lau

et al. 2021

Preprint

Self Cite

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This paper proposes a modular framework to generate robust biped locomotion using a tight coupling between an analytical walking approach and deep reinforcement learning. This framework is composed of six main modules which are hierarchically connected to reduce the overall complexity and increase its flexibility. The core of this framework is a specific dynamics model which abstracts a humanoid's dynamics model into two masses for modeling upper and lower body. This dynamics model is used to design an adaptive reference trajectories planner and an optimal controller which are fully parametric. Furthermore, a learning framework is developed based on Genetic Algorithm (GA) and Proximal Policy Optimization (PPO) to find the optimum parameters and to learn how to improve the stability of the robot by moving the arms and changing its center of mass (COM) height. A set of simulations are performed to validate the performance of the framework using the official RoboCup 3D League simulation environment. The results validate the performance of the framework, not only in creating a fast and stable gait but also in learning to improve the upper body efficiency.

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Humanoid Robot Kick in Motion Ability for Playing Robotic Soccer

Cited by 19 publications

References 24 publications

A survey of research on several problems in the RoboCup3D simulation environment

A survey of research on several problems in the RoboCup3D simulation environment

Learning by Demonstration of Coordinated Plans in Multiagent Systems

Robust Biped Locomotion Using Deep Reinforcement Learning on Top of an Analytical Control Approach

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