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pro vyhledávání: '"Bao, Lingfan"'
Achieving precise target jumping with legged robots poses a significant challenge due to the long flight phase and the uncertainties inherent in contact dynamics and hardware. Forcefully attempting these agile motions on hardware could result in seve
Externí odkaz:
http://arxiv.org/abs/2408.02619
Autor:
Peng, Tianhu, Bao, Lingfan, Humphreys, Joseph, Delfaki, Andromachi Maria, Kanoulas, Dimitrios, Zhou, Chengxu
Previous studies have successfully demonstrated agile and robust locomotion in challenging terrains for quadrupedal robots. However, the bipedal locomotion mode for quadruped robots remains unverified. This paper explores the adaptation of a learning
Externí odkaz:
http://arxiv.org/abs/2407.02282
Bipedal robots are garnering increasing global attention due to their potential applications and advancements in artificial intelligence, particularly in Deep Reinforcement Learning (DRL). While DRL has driven significant progress in bipedal locomoti
Externí odkaz:
http://arxiv.org/abs/2404.17070
Performing highly agile dynamic motions, such as jumping or running on uneven stepping stones has remained a challenging problem in legged robot locomotion. This paper presents a framework that combines trajectory optimization and model predictive co
Externí odkaz:
http://arxiv.org/abs/2204.01147