DVAC uses denoising variance as an intrinsic signal to adaptively chunk actions in flow-based robot policies, improving success rates and cutting replans on LIBERO, RoboTwin, CALVIN, and real-world tasks.
Diff-dagger: Uncertainty estimation with diffusion policy for robotic manipulation
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
CLIC uses set-valued action targets from interactive human corrections instead of pointwise labels to train more robust imitation learning policies.
RECALL introduces uncertainty-guided active data collection for continual fine-tuning of VLAs, showing efficiency gains over passive imitation but requiring replay or regularization to mitigate catastrophic forgetting.
citing papers explorer
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Denoising Tells When to Replan: Denoising-Variance Adaptive Chunking for Flow-Based Robot Policies
DVAC uses denoising variance as an intrinsic signal to adaptively chunk actions in flow-based robot policies, improving success rates and cutting replans on LIBERO, RoboTwin, CALVIN, and real-world tasks.
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From Action Labels to Sets: Rethinking Action Supervision for Imitation Learning from Corrective Feedback
CLIC uses set-valued action targets from interactive human corrections instead of pointwise labels to train more robust imitation learning policies.
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RECALL: Recovery Experience Collection for Active Lifelong Learning in Vision-Language-Action Models
RECALL introduces uncertainty-guided active data collection for continual fine-tuning of VLAs, showing efficiency gains over passive imitation but requiring replay or regularization to mitigate catastrophic forgetting.