Zobrazeno 1 - 10
of 128
pro vyhledávání: '"Ha, Sehoon"'
Autor:
Liu, Fukang, Gu, Zhaoyuan, Cai, Yilin, Zhou, Ziyi, Zhao, Shijie, Jung, Hyunyoung, Ha, Sehoon, Chen, Yue, Xu, Danfei, Zhao, Ye
Humanoid robots are designed to perform diverse loco-manipulation tasks. However, they face challenges due to their high-dimensional and unstable dynamics, as well as the complex contact-rich nature of the tasks. Model-based optimal control methods o
Externí odkaz:
http://arxiv.org/abs/2409.20514
Learning-based algorithms have demonstrated impressive performance in agile locomotion of legged robots. However, learned policies are often complex and opaque due to the black-box nature of learning algorithms, which hinders predictability and precl
Externí odkaz:
http://arxiv.org/abs/2409.14736
We present the Habitat-Matterport 3D Open Vocabulary Object Goal Navigation dataset (HM3D-OVON), a large-scale benchmark that broadens the scope and semantic range of prior Object Goal Navigation (ObjectNav) benchmarks. Leveraging the HM3DSem dataset
Externí odkaz:
http://arxiv.org/abs/2409.14296
Navigating rugged landscapes poses significant challenges for legged locomotion. Multi-legged robots (those with 6 and greater) offer a promising solution for such terrains, largely due to their inherent high static stability, resulting from a low ce
Externí odkaz:
http://arxiv.org/abs/2409.09473
Skill discovery methods enable agents to learn diverse emergent behaviors without explicit rewards. To make learned skills useful for unknown downstream tasks, obtaining a semantically diverse repertoire of skills is essential. While some approaches
Externí odkaz:
http://arxiv.org/abs/2406.06615
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:
33rd IEEE International Conference on Robot & Human Interactive Communication (RO-MAN 2024)
Integrating intelligent systems, such as robots, into dynamic group settings poses challenges due to the mutual influence of human behaviors and internal states. A robust representation of social interaction dynamics is essential for effective human-
Externí odkaz:
http://arxiv.org/abs/2404.06611
Understanding how humans leverage semantic knowledge to navigate unfamiliar environments and decide where to explore next is pivotal for developing robots capable of human-like search behaviors. We introduce a zero-shot navigation approach, Vision-La
Externí odkaz:
http://arxiv.org/abs/2312.03275
Interactive motion synthesis is essential in creating immersive experiences in entertainment applications, such as video games and virtual reality. However, generating animations that are both high-quality and contextually responsive remains a challe
Externí odkaz:
http://arxiv.org/abs/2401.06146
Control of legged robots is a challenging problem that has been investigated by different approaches, such as model-based control and learning algorithms. This work proposes a novel Imitating and Finetuning Model Predictive Control (IFM) framework to
Externí odkaz:
http://arxiv.org/abs/2311.02304