University of Michigan researchers release AFUN for robot affordance understanding
The University of Michigan-led model pairs affordance masks with 3D motion curves, but its public repo is still inference-only.
By Ryan Merket · Published
Primary source: X
Why it matters
AFUN targets a robotics bottleneck that end-to-end policies often hide: translating visual understanding into executable contact and motion. The partial release gives researchers a model to test, but the missing training, dataset, and evaluation code will determine how auditable the claim becomes.
Jun Gao (@JunGao33210520) and his PhD student Zhaoning Wang (@Zhaoning_Eric_W) are putting a sharper interface between robot perception and robot action with AFUN, a new affordance model that predicts not only where a robot should touch an object, but how it should move after contact.
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