首页|New Androids Study Findings Reported from Zhejiang University (Conditional Adver sarial Motion Priors By a Novel Retargeting Method for Versatile Humanoid Robot Control)
New Androids Study Findings Reported from Zhejiang University (Conditional Adver sarial Motion Priors By a Novel Retargeting Method for Versatile Humanoid Robot Control)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Robotics - Androids are presented in a new report. According to news reporting originating from Hangzhou , People's Republic of China, by NewsRx correspondents, research stated, "Signif icant advancements have been made in the field of humanoid robot, particularly i n walking control strategies. However, achieving straight-legged walking remains a challenge." Funders for this research include Natural Science Foundation of Zhejiang Provinc e, Natural Science Foundation of Zhejiang Province, National Natural Science Fou ndation of China (NSFC). Our news editors obtained a quote from the research from Zhejiang University, "B oth the traditional model-based and the learning-based control methods confront with difficulties in achieving natural humanoid gait feature. To address this is sue, a general motion retargeting method is developed and also evaluated for hum anoid robots with different structure, size and degrees of freedom. Moreover, a conditional adversarial motion priors method is proposed based on reinforcement learning and validated on the humanoid robot GTX-III. Through various motion seg ments from the motion capture database, it is shown that this method can success fully enable the humanoid robot to perform straight-legged walking with flexible and natural transitions between different gaits within a single discriminator n etwork. A novel motion retargeting method enables humanoid robots of varying str uctures and sizes to perform straight-legged walking with natural transitions be tween gaits."
HangzhouPeople's Republic of ChinaAsiaAndroidsEmerging TechnologiesMachine LearningNano-robotReinforcement LearningRobotRoboticsZhejiang University
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