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HIL-UMI is introduced, a policy-guided Universal Manipulation Interface (UMI) framework for robot-free human-in-the-loop VLA post-training that preserves the iterative and policy-aware nature of human-in-the-loop learning while decoupling data collection from robot deployment.
Achieving a remarkable 23% increase in navigation success rates across diverse robotic embodiments, X-NavDP redefines the potential of diffusion policies in complex environments.
Diffusion models can navigate more reliably without retraining, thanks to a clever guidance method that keeps them on the training manifold.
