BIND Action Head Ties Robot Actions to 2D Image Features for Data-Efficient Policies
yuewang314 · x · 2026-10-03
Researchers highlight a paradox in robot learning: image encoders are spatially smart — features embed semantics, geometry, and are multiview-consistent — but robot policies built on them are spatially dumb, needing hundreds of demos for simple pick-and-place and breaking under small camera shifts.
Their answer is BIND, a new action head that binds each candidate robot action to its projected 2D image feature(s), effectively telling the network "choosing this action moves the EEF to this image feature."
Result: far more data-efficient policies that are robust to OOD viewpoints and object positions, using only RGB input with no 3D sensors, outputting dense EEF trajectories.
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