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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.

22,865 Aufrufe • vor 20 Tagen •via X (Twitter)

9 Kommentare

Profilbild von Reward AI
Reward AIvor 20 Tagen

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.

Profilbild von Reward AI
Reward AIvor 20 Tagen

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.

Profilbild von Reward AI
Reward AIvor 20 Tagen

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.

Profilbild von Reward AI
Reward AIvor 20 Tagen

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

Profilbild von Reward AI
Reward AIvor 20 Tagen

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.

Profilbild von Reward AI
Reward AIvor 20 Tagen

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

Profilbild von Reward AI
Reward AIvor 20 Tagen

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.

Profilbild von Reward AI
Reward AIvor 20 Tagen

Please check out our website and blog for more information:

Profilbild von Felipe | Robot.com
Felipe | Robot.comvor 19 Tagen

Congrats!

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