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Today we’re introducing KinetIQ Ascend: our reinforcement learning approach designed to reach 99.9% manipulation reliability at human speed and beyond. It enables our robots to learn from real production tasks and improve through trial and error: • Machine feeding: throughput +42% • Item handover: throughput +85%, success from 80%... show more

Humanoid

6,016 subscribers

43,326 次观看 • 3 个月前 •via X (Twitter)

Anya Rossi• Live Now

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Humanoid 的头像

Humanoid3 个月前

Learn more about KinetIQ Ascend:

Nick Venturi 的头像

Nick Venturi3 个月前

meanwhile i still struggle to plug in a usb

Steven Cheng 的头像

Steven Cheng3 个月前

99.9% reliability at human speed is wild.

SEAR 的头像

SEAR3 个月前

99% reliability is wild

Tradeye 的头像

Tradeye3 个月前

Imitation learning is only as good as the demonstrations. We capture narrated first-person video from licensed trades technicians doing real manipulation work inside occupied American homes — high-signal tool use and decision-making in messy, unstructured environments.

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Our model can now learn from its own experience with RL! Our new π*0.6 model can more than double throughput over a base model trained without RL, and can perform real-world tasks: making espresso drinks, folding diverse laundry, and assembling boxes. More in the thread below.

Our model can now learn from its own experience with RL! Our new π*0.6 model can more than double throughput over a base model trained without RL, and can perform real-world tasks: making espresso drinks, folding diverse laundry, and assembling boxes. More in the thread below.

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