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A Control-Centric Benchmark for Video Prediction
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Anonymous ICLR 2023 submission
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A Control-Centric Benchmark for Video Prediction
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2026-02-15 00:48:33
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A Control-Centric Benchmark for Video Prediction | Anonymous ICLR 2023 submission Skip to the content. A Control-Centric Benchmark for Video Prediction Anonymous ICLR 2023 submission Abstract Video is a promising source of knowledge for embodied agents to learn models of the world’s dynamics. Large deep networks have become increasingly effective at modeling complex video data in a self-supervised manner, as evaluated by metrics based on human perceptual similarity or pixel-wise comparison. However, it remains unclear whether current metrics are accurate indicators of performance on downstream tasks. We find empirically that for planning robotic manipulation, existing metrics can be unreliable at predicting execution success. To address this, we propose a benchmark for action-conditioned video prediction in the form of a control benchmark that evaluates a given model for simulated robotic manipulation through sampling-based planning. Our benchmark, Video Prediction for Visua...
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