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Efficiency is ROI. ROI is what gets robots deployed. OM-1 makes long-horizon tasks (quadmanual phone packaging, bartending, laundry folding) look like easy <30s jobs. All our videos are real-time with no speedup.

Reward AI

4,853 subscribers

22,865 просмотров • 20 дней назад •via X (Twitter)

Anya Rossi• Live Now

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Комментарии: 9

Фото профиля Reward AI

Reward AI20 дней назад

We built Omnibody Hand around function: useful contact points, in-hand reorientation, and smooth transitions between precision and power grasps. Its compact 7-DoF design combines thumb-index dexterity with coordinated finger motion for power grasps.

Фото профиля Reward AI

Reward AI20 дней назад

OM-1 zero-shot generalizes across different kinds of embodiments: small arms, industrial arms, and even humanoids, with our 7-DoF Omnibody Hands. No robot-specific finetuning needed. In a time when hardware advances every month, robot models and data need to be future-proof.

Фото профиля Reward AI

Reward AI20 дней назад

OM-1 captures subconscious human physical intelligence directly from humans: nudging, sliding, twisting, power grasping, and whole-body coordination. We build on our prior work DexCap ( at Stanford.

Фото профиля Reward AI

Reward AI20 дней назад

Introducing OM-1, our first robot foundation model, zero-shot generalizing to any robot: table-top arms, industrial arms and humanoids. - learned directly from human manipulation data - no teleop/robot data - close to human-level dexterity and efficiency - multi-robot collab

Фото профиля Reward AI

Reward AI20 дней назад

We also tested OM-1 on a deceptively hard task: unplugging an Ethernet cable. Unlike USB, Ethernet connectors lock firmly in place and release only when the latch is pressed very precisely. OM-1 reliably solves this fine-grained manipulation task.

Фото профиля Reward AI

Reward AI20 дней назад

OM-1 can power humanoids to do whole-body manipulation and navigation. More on this later.

Фото профиля Reward AI

Reward AI20 дней назад

OM-1 exhibits several emergent behaviors. Each arm learns to compensate for the other’s mistakes. The model knows when to retry, when to adapt to adversarial perturbations, and when to stop given the environment changes too drastically.

Фото профиля Reward AI

Reward AI20 дней назад

Please check out our website and blog for more information:

Фото профиля Felipe | Robot.com

Felipe | Robot.com19 дней назад

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