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MAESTRO is used as a coding agent that writes robot programs using a curated library of perception, planning, and control tools and its orchestration harness packages multi-step perception and action procedures into callable tools and guides their use through a plan–act– replan loop.
IMLE-VLA is introduced, which replaces the iterative action head with a single-step conditional generator trained via conditional Implicit Maximum Likelihood Estimation (cIMLE), and promotes multimodal action coverage, avoiding the mode collapse of naive regression heads while eliminating multi-step sampling entirely.
P2R tracks task-relevant object points in task demonstrations and policy rollouts, and matches them to compare the desired and achieved object trajectories to generate reward scores, which effectively computes dense rewards from a single video.
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