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Nishanth Kumar

@nishanthkumar23 • 1,994 subscribers

Research @Meta Robotics Studio. Ph.D. @MIT_LISLab. Formerly @AIatMeta., @NVIDIAAI, @rai_inst, @vicariousai and @uber; Sc.B. @BrownUniversity.

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State-of-the-art robot policies often need hundreds of hours of data. What if we needed none? Introducing TiPToP: a manipulation system that zero-shots open-world tasks from pixels and language using vision foundation models and GPU-parallelized Task and Motion Planning (TAMP).

State-of-the-art robot policies often need hundreds of hours of data. What if we needed none? Introducing TiPToP: a manipulation system that zero-shots open-world tasks from pixels and language using vision foundation models and GPU-parallelized Task and Motion Planning (TAMP).

78,659 次观看 • 5 个月前

World models hold a lot of promise for robotics, but they're data hungry and often struggle with long horizons. We learn models from a few (< 10) human demos that enable a robot to plan in completely novel scenes! Our key idea is to model *symbols* not pixels 👇

World models hold a lot of promise for robotics, but they're data hungry and often struggle with long horizons. We learn models from a few (< 10) human demos that enable a robot to plan in completely novel scenes! Our key idea is to model symbols not pixels 👇

82,931 次观看 • 11 个月前

Can we get robots to improve at long-horizon tasks without supervision? Our latest work tackles this problem by planning to practice! Here's a teaser showing initial task -> autonomous practice -> eval (+ interference by a gremlin👿)

Can we get robots to improve at long-horizon tasks without supervision? Our latest work tackles this problem by planning to practice! Here's a teaser showing initial task -> autonomous practice -> eval (+ interference by a gremlin👿)

18,722 次观看 • 2 年前

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