Elastic-WAM: Adaptive Spatiotemporal Imagination for Efficient Robot Control
Overview
This research paper proposes Elastic-WAM, a novel approach for efficient robot control that adaptively allocates world refinement computation across temporal denoising and spatial future space. By parameterizing a unified spatiotemporal refinement demand field learned through action-grounded supervision, the method guides recurrent sparse world refinement to improve task success and reduce inference latency. Evaluations on diverse simulation benchmarks and real-world long-horizon manipulation tasks demonstrate state-of-the-art control performance with reduced computational overhead compared to prior iterative video denoising approaches.
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- arXiv Robotics — research abstractsSignalElastic-WAM: Adaptive Spatiotemporal Imagination for Efficient Robot Control
This research paper proposes Elastic-WAM, a novel approach for efficient robot control that adaptively allocates world refinement computation across temporal denoising and spatial future space. By parameterizing a unified spatiotemporal refinement demand field learned through action-grounded supervision, the method guides recurrent sparse world refinement to improve task success and reduce inference latency. Evaluations on diverse simulation benchmarks and real-world long-horizon manipulation tasks demonstrate state-of-the-art control performance with reduced computational overhead compared to prior iterative video denoising approaches.
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