Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
Robot control is formulated as conditional denoising of action sequences, linking diffusion models to visuomotor learning.
A task-level analysis of robot exposure that distinguishes demonstrated capability in limited settings from general deployment.
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
A bridge between embodied capabilities and practical applications, not a forecast of automatic job elimination.
@misc{anthropic2026what,
title = {What work can robots do?},
author = {Anthropic},
year = {2026},
url = {https://www.anthropic.com/research/what-work-can-robots-do}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
Robot control is formulated as conditional denoising of action sequences, linking diffusion models to visuomotor learning.
Vision-language models are trained to produce robot actions, exploring transfer from web-scale learning to robotic control.
An open vision-language-action model and adaptation framework make generalist robot policy research more accessible.