Discrete diffusion policies act as natural asynchronous executors for robotics by treating action generation as iterative unmasking, yielding higher success rates and lower computation than flow-matching real-time chunking in dynamic tasks.
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Encoding user interactions into visual in-context example pairs turns static models into controllable systems that improve IoU, PSNR, and LPIPS on guided tasks without retraining.
citing papers explorer
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DiscreteRTC: Discrete Diffusion Policies are Natural Asynchronous Executors
Discrete diffusion policies act as natural asynchronous executors for robotics by treating action generation as iterative unmasking, yielding higher success rates and lower computation than flow-matching real-time chunking in dynamic tasks.
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From Static to Interactive: Adapting Visual in-Context Learners for User-Driven Tasks
Encoding user interactions into visual in-context example pairs turns static models into controllable systems that improve IoU, PSNR, and LPIPS on guided tasks without retraining.