مقاله انگلیسی رایگان در مورد رفتار ربات های انسان نما و یادگیری تقویتی – اسپرینگر ۲۰۲۲

مقاله انگلیسی رایگان در مورد رفتار ربات های انسان نما و یادگیری تقویتی – اسپرینگر ۲۰۲۲

 

مشخصات مقاله
ترجمه عنوان مقاله یادگیری تقویتی عمیق برای رفتارهای ربات انسان نما
عنوان انگلیسی مقاله Deep Reinforcement Learning for Humanoid Robot Behaviors
انتشار مقاله سال ۲۰۲۲
تعداد صفحات مقاله انگلیسی  ۱۶ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه اسپرینگر
نوع نگارش مقاله
مقاله پژوهشی (Research article)
مقاله بیس این مقاله بیس میباشد
نمایه (index) JCR – Master Journal List – Scopus – ISC
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۳٫۶۱۱ در سال ۲۰۲۰
شاخص H_index ۸۲ در سال ۲۰۲۲
شاخص SJR ۰٫۸۱۶ در سال ۲۰۲۰
شناسه ISSN ۱۵۷۳-۰۴۰۹
شاخص Quartile (چارک) Q1 در سال ۲۰۲۰
فرضیه ندارد
مدل مفهومی دارد
پرسشنامه ندارد
متغیر دارد
رفرنس دارد
رشته های مرتبط مهندسی کامپیوتر – مهندسی برق
گرایش های مرتبط هوش مصنوعی – مهندسی نرم افزار – رباتیک – مهندسی الکترونیک – مهندسی کنترل
نوع ارائه مقاله
ژورنال
مجله / کنفرانس مجله سیستم های هوشمند و رباتیک – Journal of Intelligent & Robotic Systems
دانشگاه Computer Science Division, Aeronautics Institute of Technology, Brazil
کلمات کلیدی یادگیری تقویتی عمیق – ربات فوتبال – ربات های انسان نما – رباتیک
کلمات کلیدی انگلیسی Deep reinforcement learning – Robot soccer – Humanoid robots – Robotics
شناسه دیجیتال – doi
https://doi.org/10.1007/s10846-022-01619-y
کد محصول e16643
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:

Abstract

۱ Introduction

۲ Theoretical Background

۳ Related Works

۴ Methodology

۵ Experiments and Results

۶ Conclusions

Declarations

References

 

بخشی از متن مقاله:

Abstract

     RoboCup 3D Soccer Simulation is a robot soccer competition based on a high-fidelity simulator with autonomous humanoid agents, making it an interesting testbed for robotics and artificial intelligence. Due to the recent success of Deep Reinforcement Learning (DRL) in continuous control tasks, many teams have been using this technique to develop motions in Soccer 3D. This article focuses on learning humanoid robot behaviors: completing a racing track as fast as possible and dribbling against a single opponent. Our approach uses a hierarchical controller where a model-free policy learns to interact model-based walking algorithm. Then, we use DRL algorithms for an agent to learn how to perform these behaviors. Finally, the learned dribble policy was evaluated in the Soccer 3D environment. Simulated experiments show that the DRL agent wins against the hand-coded behavior used by the ITAndroids robotics team in 68.2% of dribble attempts.

Introduction

     RoboCup is an international academic competition created to foster robotics and artificial intelligence research [27]. It has an ambitious long-term goal of having a team of humanoid robots beating the human soccer World Cup champions by 2050. There are many leagues with different game rules and constraints on robot designs to accelerate progress towards this objective.

RoboCup 3D Soccer Simulation (Soccer 3D) is a league of RoboCup based on a robot soccer simulator with a high-fidelity simulation model of the Nao humanoid robot. The particular contributions to RoboCup reside in being a research environment for high-level multi-agent cooperative decision-making, and humanoid robot control [44]. A simulation environment is convenient for machine learning algorithms due to their need for large amounts of data [36]. Dealing with real robots is time-consuming due to the need to recharge batteries or reallocate robots manually to set up experiments. Moreover, experience collection may be largely accelerated by running many simulations in parallel and executing in faster than real-time. Unfortunately, transferring behaviors learned in simulation to real robots is challenging due to the so-called reality gap. Still, some works have succeeded in doing so, usually by executing a final fine-tuning process on the real robot [36].

Conclusions

     Our main objective was to learn high-level soccer behaviors using reinforcement learning in this work. We addressed the problem with state-of-the-art model-free deep reinforcement learning algorithms, namely DDPG, TRPO, and PPO. Therefore, we learned behaviors while dealing with the complex dynamics of a humanoid robot.

     To facilitate, we used a hierarchical approach where the agent learns to command a model-based walking engine based on the Zero Moment Point (ZMP) concept. The walking engine receives the desired velocities in forward, lateral and rotational directions and outputs the joint angles.

     We developed a DRL framework for integrating DRL algorithms with the RoboCup 3D Soccer Simulation environment to accomplish our objective. In our results, PPO achieved the best performance, which was expected, and effectively learned humanoid robot behaviors.

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