ReAT: Residual Action Tokenization for Autoregressive Robot
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: ReAT: Residual Action Tokenization for Autoregressive Robot Policies. This research proposes ReAT, a residual action tokenization method that enables each token to explicitly refine the current action reconstruction. By using decoded reconstruction as feedback, ReAT closes the loop between…
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- arXiv Robotics — research abstractsReAT: Residual Action Tokenization for Autoregressive Robot Policies
This research proposes ReAT, a residual action tokenization method that enables each token to explicitly refine the current action reconstruction. By using decoded reconstruction as feedback, ReAT closes the loop between encoding and decoding, allowing tokens to respond to remaining action errors. Evaluated across 19 manipulation tasks, ReAT policies outperform baselines in task success, with even 4-token ReAT surpassing 8-token baselines on LIBERO at half the token budget.
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