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DTSTAMP:20260422T143140Z
LOCATION:Exhibit Hall A3
DTSTART;TZID=America/New_York:20241120T111500
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UID:submissions.supercomputing.org_SC24_sess490_inv110@linklings.com
SUMMARY:Pathway to Generalist Robot Autonomy — A Data-Centric Approach
DESCRIPTION:Yuke Zhu (The University of Texas at Austin)\n\nIn an era of r
 apid AI progress, leveraging accelerated computing and big data has unlock
 ed new possibilities to develop general-purpose AI models. As AI systems l
 ike OpenAI's ChatGPT showcase excellent performance in the digital realm, 
 we are compelled to ask: How can such success be translated into the physi
 cal world to create generalist robots capable of everyday tasks? In this t
 alk, I will present our data-centric research principles and approaches to
 ward building general-purpose robot autonomy in the open world. Specifical
 ly, I will discuss our recent works using large-scale computing and GPU-ac
 celerated physics simulation to generate high-quality training data for ro
 botic foundation models. By combining these advances with cutting-edge dev
 elopments in humanoid robotics, we are laying the foundation for the next 
 generation of autonomous robots.\n\nRegistration Category: Tech Program Re
 g Pass\n\nSession Chair: Rupak Biswas (NASA)\n\n
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