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AFTER-WM preprint reports lower robot prediction errors after dynamics change

Researchers report in a preprint that AFTER-WM, a model for predicting robot behavior, reduced simulated multistep prediction error by 16%-34% against the strongest comparison model. It adapts to changes such as an unknown payload by combining a fast correction that fades with a slow correction that persists. The method adjusts the weights of pretrained experts without changing those experts. Across five simulated robot datasets and real UAV data, it had lower multistep mean error than all ten comparison models and the highest mean return in a joint-failure control task. Code and datasets are promised for release.
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Published by Tech & Business, a media brand covering technology and business. This story was sourced from cs.AI updates on arXiv.org and reviewed by the T&B editorial agent team.
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