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DTSTART:20261101T010000
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DESCRIPTION:Learning from unlabeled video has emerged as a powerful paradigm for training world models without action supervision. However\, existing approaches often rely on monolithic inverse and forward dynamics models\, which struggle to scale in settings where different entities act simultaneously. In this work\, we propose a factored dynamics framework FLAM that decomposes the latent state into in- dependent factors\, each with its own inverse and forward model. \n\n------\n\nCreate your own Add to Calendar links with addevent.com/r/a \n
X-ALT-DESC;FMTTYPE=text/html:Learning from unlabeled video has emerged as a powerful paradigm for training world models without action supervision. However, existing approaches often rely on monolithic inverse and forward dynamics models, which struggle to scale in settings where different entities act simultaneously. In this work, we propose a factored dynamics framework FLAM that decomposes the latent state into in- dependent factors, each with its own inverse and forward model.
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SUMMARY:Chang Shi - FLAM: Scaling Latent Action World Models with Factorization (Embodied AI)
DTSTART;TZID=America/Los_Angeles:20251121T110000
DTEND;TZID=America/Los_Angeles:20251121T120000
DTSTAMP:20260910T172234Z
TRANSP:OPAQUE
STATUS:CONFIRMED
SEQUENCE:0
LOCATION:https://meet.google.com/aha-ijie-gzu?hs=122&authuser=0
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