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Check out my most recent project that I've been working on for more than half a year -- co-led with Tim @TimSong52005757!
We showed a flexible and effective recipe for improving generalist VLAs leveraging non-robotic foundation models! The key is guidance in 3D!
More details @ omniguide.github.io
We showed a flexible and effective recipe for improving generalist VLAs leveraging non-robotic foundation models! The key is guidance in 3D!
More details @ omniguide.github.io
VLA policies learn generalist robot behaviors from massive teleoperation datasets, hoping that the right behavior emerges. But they rarely use perception during training or inference: powerful foundation models of 3D geometry, semantics, or human motion are ignored.View quoted post on X
@TimSong52005757, @LongLeRobot, and our @GRASPlab team introduce Omniguide, based on a simple idea: Instead of retraining policies, let perception guide action at inference time.
We express diverse guidance sources as attractive and repulsive energy fields in 3D space and inject their gradients into the generative process of VLA policies.
This lets perception modules steer actions without retraining the policy: A modular path toward composable robot intelligence.
Project: omniguide.github.io
Paper: arxiv.org/abs/2603.10052