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pro vyhledávání: '"Fei, Li"'
Representing robotic manipulation tasks as constraints that associate the robot and the environment is a promising way to encode desired robot behaviors. However, it remains unclear how to formulate the constraints such that they are 1) versatile to
Externí odkaz:
http://arxiv.org/abs/2409.01652
Most existing human rendering methods require every part of the human to be fully visible throughout the input video. However, this assumption does not hold in real-life settings where obstructions are common, resulting in only partial visibility of
Externí odkaz:
http://arxiv.org/abs/2407.00316
Autor:
Durante, Zane, Harries, Robathan, Vendrow, Edward, Luo, Zelun, Kyuragi, Yuta, Kozuka, Kazuki, Fei-Fei, Li, Adeli, Ehsan
Understanding Activities of Daily Living (ADLs) is a crucial step for different applications including assistive robots, smart homes, and healthcare. However, to date, few benchmarks and methods have focused on complex ADLs, especially those involvin
Externí odkaz:
http://arxiv.org/abs/2406.01662
Learning in simulation and transferring the learned policy to the real world has the potential to enable generalist robots. The key challenge of this approach is to address simulation-to-reality (sim-to-real) gaps. Previous methods often require doma
Externí odkaz:
http://arxiv.org/abs/2405.10315
Autor:
Ge, Yunhao, Tang, Yihe, Xu, Jiashu, Gokmen, Cem, Li, Chengshu, Ai, Wensi, Martinez, Benjamin Jose, Aydin, Arman, Anvari, Mona, Chakravarthy, Ayush K, Yu, Hong-Xing, Wong, Josiah, Srivastava, Sanjana, Lee, Sharon, Zha, Shengxin, Itti, Laurent, Li, Yunzhu, Martín-Martín, Roberto, Liu, Miao, Zhang, Pengchuan, Zhang, Ruohan, Fei-Fei, Li, Wu, Jiajun
The systematic evaluation and understanding of computer vision models under varying conditions require large amounts of data with comprehensive and customized labels, which real-world vision datasets rarely satisfy. While current synthetic data gener
Externí odkaz:
http://arxiv.org/abs/2405.09546
Autor:
Li, Chengshu, Zhang, Ruohan, Wong, Josiah, Gokmen, Cem, Srivastava, Sanjana, Martín-Martín, Roberto, Wang, Chen, Levine, Gabrael, Ai, Wensi, Martinez, Benjamin, Yin, Hang, Lingelbach, Michael, Hwang, Minjune, Hiranaka, Ayano, Garlanka, Sujay, Aydin, Arman, Lee, Sharon, Sun, Jiankai, Anvari, Mona, Sharma, Manasi, Bansal, Dhruva, Hunter, Samuel, Kim, Kyu-Young, Lou, Alan, Matthews, Caleb R, Villa-Renteria, Ivan, Tang, Jerry Huayang, Tang, Claire, Xia, Fei, Li, Yunzhu, Savarese, Silvio, Gweon, Hyowon, Liu, C. Karen, Wu, Jiajun, Fei-Fei, Li
We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an extensive survey on "what do you want robots to do for you?". The first is the de
Externí odkaz:
http://arxiv.org/abs/2403.09227
Imitation learning from human hand motion data presents a promising avenue for imbuing robots with human-like dexterity in real-world manipulation tasks. Despite this potential, substantial challenges persist, particularly with the portability of exi
Externí odkaz:
http://arxiv.org/abs/2403.07788
Autor:
Huang, Qiuyuan, Wake, Naoki, Sarkar, Bidipta, Durante, Zane, Gong, Ran, Taori, Rohan, Noda, Yusuke, Terzopoulos, Demetri, Kuno, Noboru, Famoti, Ade, Llorens, Ashley, Langford, John, Vo, Hoi, Fei-Fei, Li, Ikeuchi, Katsu, Gao, Jianfeng
Recent advancements in large foundation models have remarkably enhanced our understanding of sensory information in open-world environments. In leveraging the power of foundation models, it is crucial for AI research to pivot away from excessive redu
Externí odkaz:
http://arxiv.org/abs/2403.00833
Autor:
Durante, Zane, Sarkar, Bidipta, Gong, Ran, Taori, Rohan, Noda, Yusuke, Tang, Paul, Adeli, Ehsan, Lakshmikanth, Shrinidhi Kowshika, Schulman, Kevin, Milstein, Arnold, Terzopoulos, Demetri, Famoti, Ade, Kuno, Noboru, Llorens, Ashley, Vo, Hoi, Ikeuchi, Katsu, Fei-Fei, Li, Gao, Jianfeng, Wake, Naoki, Huang, Qiuyuan
The development of artificial intelligence systems is transitioning from creating static, task-specific models to dynamic, agent-based systems capable of performing well in a wide range of applications. We propose an Interactive Agent Foundation Mode
Externí odkaz:
http://arxiv.org/abs/2402.05929
Autor:
Durante, Zane, Huang, Qiuyuan, Wake, Naoki, Gong, Ran, Park, Jae Sung, Sarkar, Bidipta, Taori, Rohan, Noda, Yusuke, Terzopoulos, Demetri, Choi, Yejin, Ikeuchi, Katsushi, Vo, Hoi, Fei-Fei, Li, Gao, Jianfeng
Multi-modal AI systems will likely become a ubiquitous presence in our everyday lives. A promising approach to making these systems more interactive is to embody them as agents within physical and virtual environments. At present, systems leverage ex
Externí odkaz:
http://arxiv.org/abs/2401.03568