SURE: Spatiotemporal Uncertainty Representation for Efficient Vision-Language-Action Learning
Overview
This research introduces SURE, a data-efficient training framework for vision-language-action (VLA) policies that explicitly models spatial and temporal uncertainty in action spaces. By augmenting a VLA backbone with a probabilistic action decoder using local correlations and employing Soft-DTW alignment supervision to handle timing variations, SURE reduces sensitivity to demonstrator-specific poses and execution speeds. Evaluations on LIBERO, RoboTwin 2.0, and real-world manipulation tasks show that SURE achieves success rates comparable to or better than baseline VLAs even when using only 10% of demonstration data or 20% of training steps.
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- arXiv Robotics — research abstractsSignalSURE: Spatiotemporal Uncertainty Representation for Efficient Vision-Language-Action Learning
This research introduces SURE, a data-efficient training framework for vision-language-action (VLA) policies that explicitly models spatial and temporal uncertainty in action spaces. By augmenting a VLA backbone with a probabilistic action decoder using local correlations and employing Soft-DTW alignment supervision to handle timing variations, SURE reduces sensitivity to demonstrator-specific poses and execution speeds. Evaluations on LIBERO, RoboTwin 2.0, and real-world manipulation tasks show that SURE achieves success rates comparable to or better than baseline VLAs even when using only 10% of demonstration data or 20% of training steps.
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