Zobrazeno 1 - 10
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pro vyhledávání: '"Smith, Laura P."'
Autor:
Bullock, Seth, Ajmeri, Nirav, Batty, Mike, Black, Michaela, Cartlidge, John, Challen, Robert, Chen, Cangxiong, Chen, Jing, Condell, Joan, Danon, Leon, Dennett, Adam, Heppenstall, Alison, Marshall, Paul, Morgan, Phil, O'Kane, Aisling, Smith, Laura G. E., Smith, Theresa, Williams, Hywel T. P.
Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or trans-national scale. Pressing challenges in healthcare, finance, infrastructure and susta
Externí odkaz:
http://arxiv.org/abs/2411.06211
Autor:
Smith, Laura, Irpan, Alex, Arenas, Montserrat Gonzalez, Kirmani, Sean, Kalashnikov, Dmitry, Shah, Dhruv, Xiao, Ted
The complexity of the real world demands robotic systems that can intelligently adapt to unseen situations. We present STEER, a robot learning framework that bridges high-level, commonsense reasoning with precise, flexible low-level control. Our appr
Externí odkaz:
http://arxiv.org/abs/2411.03409
Autor:
Nasiriany, Soroush, Kirmani, Sean, Ding, Tianli, Smith, Laura, Zhu, Yuke, Driess, Danny, Sadigh, Dorsa, Xiao, Ted
We explore how intermediate policy representations can facilitate generalization by providing guidance on how to perform manipulation tasks. Existing representations such as language, goal images, and trajectory sketches have been shown to be helpful
Externí odkaz:
http://arxiv.org/abs/2411.02704
Autor:
Zhang, Hongbo, Li, Zhongyu, Zeng, Xuanqi, Smith, Laura, Stachowicz, Kyle, Shah, Dhruv, Yue, Linzhu, Song, Zhitao, Xia, Weipeng, Levine, Sergey, Sreenath, Koushil, Liu, Yun-hui
The enhanced mobility brought by legged locomotion empowers quadrupedal robots to navigate through complex and unstructured environments. However, optimizing agile locomotion while accounting for the varying energy costs of traversing different terra
Externí odkaz:
http://arxiv.org/abs/2410.10621
Autor:
Rafailov, Rafael, Hatch, Kyle, Singh, Anikait, Smith, Laura, Kumar, Aviral, Kostrikov, Ilya, Hansen-Estruch, Philippe, Kolev, Victor, Ball, Philip, Wu, Jiajun, Finn, Chelsea, Levine, Sergey
Offline reinforcement learning algorithms hold the promise of enabling data-driven RL methods that do not require costly or dangerous real-world exploration and benefit from large pre-collected datasets. This in turn can facilitate real-world applica
Externí odkaz:
http://arxiv.org/abs/2408.08441
Autor:
Huang, Xiaoyu, Liao, Qiayuan, Ni, Yiming, Li, Zhongyu, Smith, Laura, Levine, Sergey, Peng, Xue Bin, Sreenath, Koushil
This work presents HiLMa-Res, a hierarchical framework leveraging reinforcement learning to tackle manipulation tasks while performing continuous locomotion using quadrupedal robots. Unlike most previous efforts that focus on solving a specific task,
Externí odkaz:
http://arxiv.org/abs/2407.06584
Autor:
Chen, Annie S., Lessing, Alec M., Tang, Andy, Chada, Govind, Smith, Laura, Levine, Sergey, Finn, Chelsea
Legged robots are physically capable of navigating a diverse variety of environments and overcoming a wide range of obstructions. For example, in a search and rescue mission, a legged robot could climb over debris, crawl through gaps, and navigate ou
Externí odkaz:
http://arxiv.org/abs/2407.02666
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
Autor:
Chen, Annie S., Chada, Govind, Smith, Laura, Sharma, Archit, Fu, Zipeng, Levine, Sergey, Finn, Chelsea
To succeed in the real world, robots must cope with situations that differ from those seen during training. We study the problem of adapting on-the-fly to such novel scenarios during deployment, by drawing upon a diverse repertoire of previously lear
Externí odkaz:
http://arxiv.org/abs/2311.01059
Deep reinforcement learning (RL) can enable robots to autonomously acquire complex behaviors, such as legged locomotion. However, RL in the real world is complicated by constraints on efficiency, safety, and overall training stability, which limits i
Externí odkaz:
http://arxiv.org/abs/2310.17634