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pro vyhledávání: '"Zargarbashi, Fatemeh"'
This paper presents a novel learning-based control framework that uses keyframing to incorporate high-level objectives in natural locomotion for legged robots. These high-level objectives are specified as a variable number of partial or complete pose
Externí odkaz:
http://arxiv.org/abs/2407.11562
Autor:
Zargarbashi, Fatemeh, Di Giuro, Fabrizio, Cheng, Jin, Kang, Dongho, Sukhija, Bhavya, Coros, Stelian
This work presents a meta-reinforcement learning approach to develop a universal locomotion control policy capable of zero-shot generalization across diverse quadrupedal platforms. The proposed method trains an RL agent equipped with a memory unit to
Externí odkaz:
http://arxiv.org/abs/2407.17502
This paper presents a control framework that combines model-based optimal control and reinforcement learning (RL) to achieve versatile and robust legged locomotion. Our approach enhances the RL training process by incorporating on-demand reference mo
Externí odkaz:
http://arxiv.org/abs/2305.17842
Publikováno v:
Iranian Journal of Science and Technology. Transactions of Mechanical Engineering; December 2024, Vol. 48 Issue: 4 p1597-1605, 9p
Akademický článek
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