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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.
This work proposes a plug-and-play fix to automatically generate scene-specific, task-specific, adaptively updating abstractions on top of existing planning and control stacks, and shows that LENS improves classical planning, model-based control, and a vision-language-action model, across a diverse set of highly clutte...
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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