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of 76
pro vyhledávání: '"Xie Zhaoming"'
Legged locomotion holds the premise of universal mobility, a critical capability for many real-world robotic applications. Both model-based and learning-based approaches have advanced the field of legged locomotion in the past three decades. In recen
Externí odkaz:
http://arxiv.org/abs/2406.01152
Publikováno v:
In SIGGRAPH Asia 2024 Conference Papers (Article No. 86, 10 pages)
Generating diverse and realistic human motion that can physically interact with an environment remains a challenging research area in character animation. Meanwhile, diffusion-based methods, as proposed by the robotics community, have demonstrated th
Externí odkaz:
http://arxiv.org/abs/2406.00960
Humans perform everyday tasks using a combination of locomotion and manipulation skills. Building a system that can handle both skills is essential to creating virtual humans. We present a physically-simulated human capable of solving box rearrangeme
Externí odkaz:
http://arxiv.org/abs/2306.09532
Recent advances in deep reinforcement learning (RL) based techniques combined with training in simulation have offered a new approach to developing robust controllers for legged robots. However, the application of such approaches to real hardware has
Externí odkaz:
http://arxiv.org/abs/2303.03724
Reinforcement Learning (RL) has seen many recent successes for quadruped robot control. The imitation of reference motions provides a simple and powerful prior for guiding solutions towards desired solutions without the need for meticulous reward des
Externí odkaz:
http://arxiv.org/abs/2210.01247
Model-free reinforcement learning (RL) for legged locomotion commonly relies on a physics simulator that can accurately predict the behaviors of every degree of freedom of the robot. In contrast, approximate reduced-order models are commonly used for
Externí odkaz:
http://arxiv.org/abs/2104.09771
Autor:
Zhang, Yue a, 1, Li, Moshan a, 1, Liu, Zuohua a, Yu, Jianglong b, c, Zheng, Guocan a, Ma, Youcai a, Xie, Zhaoming a, Tao, Changyuan a, Qu, Rui a, Li, Shuai a, Hu, Erfeng a, ⁎
Publikováno v:
In Separation and Purification Technology 1 May 2025 357 Part A
Understanding the gap between simulation and reality is critical for reinforcement learning with legged robots, which are largely trained in simulation. However, recent work has resulted in sometimes conflicting conclusions with regard to which facto
Externí odkaz:
http://arxiv.org/abs/2011.02404
Autor:
Da, Xingye, Xie, Zhaoming, Hoeller, David, Boots, Byron, Anandkumar, Animashree, Zhu, Yuke, Babich, Buck, Garg, Animesh
We present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped (the Unitree Laikago). The system consists of a high-level controller that learns to choose from a
Externí odkaz:
http://arxiv.org/abs/2009.10019
Humans are highly adept at walking in environments with foot placement constraints, including stepping-stone scenarios where the footstep locations are fully constrained. Finding good solutions to stepping-stone locomotion is a longstanding and funda
Externí odkaz:
http://arxiv.org/abs/2005.04323