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Robots Must Focus on Control-Relevant Features Over Visual Distractions

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Furong Huang#465
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Furong Huang@furongh#465inAI

Imagine your home robot is pouring almonds while a horror movie is playing on your TV.

The vampire jumps out.

A good robot should not flinch, panic, or throw almonds everywhere 馃槀

It should know: the TV is visually salient, but control-irrelevant.

Furong Huang@furongh

This distinction matters.

A vision model may care about the cup logo, table texture, shadows, background objects, lighting, or a TV in the corner.

A robot should care about something much narrower:

the hand, the object, the contact region, and the motion that follows.

10:04 AM 路 Jun 1, 2026 路 78 Views
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Furong Huang@furongh

The broader message:

As the field scales robot foundation models and VLAs, we should not leave perception as an afterthought.

Bigger robot brains still need the right eyes.

And for robots, the right eyes are not just semantic.

They are action-aware.

Furong Huang@furongh

This improves downstream robot learning across simulation and real-world manipulation.

DynaFLIP works as a reusable encoder for different policies, including MLP policies, diffusion policies, and VLAs, with gains up to +22.5% in OOD settings.

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Furong Huang@furongh

DynaFLIP uses three training-time signals:

image transitions, language, and 3D flow.

Together, they teach the encoder:

what changed, what the change means, and where physical motion happened. At deployment, the robot still only needs images.

Refer to @jusukle Jusuk's thread for more details:

Furong Huang@furongh

That is the core idea behind our paper, DynaFLIP.

Instead of pushing all motion understanding into the downstream policy, we move it upstream into the visual encoder.

The encoder is pre-trained to represent not just what is present, but how the world changes under action.

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Furong Huang@furongh

This improves downstream robot learning across simulation and real-world manipulation.

DynaFLIP works as a reusable encoder for different policies, including MLP policies, diffusion policies, and VLAs, with gains up to +22.5% in OOD settings.

Furong Huang@furongh

The result is a more robotics-native visual backbone.

Instead of attending broadly to visual detail, DynaFLIP focuses more on control-relevant regions: manipulated objects, contact areas, and parts of the scene that actually matter for manipulation.

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