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TU Darmstadt's Georgia Chalvatzaki argues physical contact makes robot action spaces complex, while Jon Barron defends end-to-end image-to-action learning

Barron argues image-to-action pipelines bypass intermediate 3D reconstructions.

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Georgia Chalvatzaki
Jon Barron#261
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Georgia Chalvatzaki@GeorgiaChal#1888inAI

The action spacein robotics isn't small! That's the misconception of many. Contact makes the dynamics hybrid: you switch manifolds every time a contact makes or breaks, and the optimization landscape is non-smooth precisely there. A low-dimensional command vector is not a small problem. That difficulty is the SE(3) geometry you're waving away!

Jon Barron@jon_barron

I'll be giving a "exactly how bitter lesson'ed is all of 3D computer vision?" talk at the Bitter Lessons CVPR workshop tomorrow at 10:30am, Room 3A-3D. Here's a slide with the overall thesis.

8:37 AM · Jun 3, 2026 · 366 Views
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Jon Barron@jon_barron

@GeorgiaChal oh cool, I'll try to make it!

Just to clarify, I don't think robotics is easy in absolute terms, what I'm asserting here is that the problem of predicting actions from images is easier than the problem of predicting 3D from images and then predicting actions from 3D.

Georgia Chalvatzaki@GeorgiaChal

"The space of actions is tiny" — that's what he said... A 7-DoF command is low-dimensional, but the problem isn't. Contact makes the dynamics hybrid and non-smooth; you're optimizing across manifold switches every time something touches. That hardness is the geometry being waved away.

Conveniently, the field is debating exactly this on Friday! Come weigh in: "Geometry in the Age of Data-Driven Robotics," #ICRA2026, Hall C4, Fri Jun 5. https://geometric-robotics.github.io/icra-2026-workshop/

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