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Robots can learn to collaborate effectively without synchronized demonstrations, achieving better coordination through a structured imitation curriculum.
PATCH enables robots to autonomously pause and recover from disturbances, improving manipulation stability in unpredictable environments.
Robots can now map environments and know what they *don't* know in real-time, without the computational overhead of Bayesian methods.
Streaming flow policies can now be adapted in real-time to enforce safety and task-specific constraints, ensuring robots respect joint limits and avoid collisions without sacrificing smoothness or reactivity.
Generalizing robot skills across geometrically diverse objects is now more practical: SemFM enables affordance transfer from a single visual demonstration by establishing dense, semantically consistent correspondence between object instances.
