ReAT：用于自回归机器人策略的残差动作分词
ReAT: Residual Action Tokenization for Autoregressive Robot Policies
AI总结：
该研究提出ReAT残差动作分词方法，通过闭合编码解码循环优化动作重构，在19个操作任务中提升了自回归机器人策略的任务成功率，为自校正策略学习提供了可行路径。
摘要：
自回归机器人策略依赖动作分词器将连续动作块转换为紧凑的离散预测目标序列。现有分词器可从离散代码重构动作块，但通常未明确每个令牌对重构动作的贡献。我们提出残差动作分词（Residual Action Tokenization，ReAT），为每个令牌赋予明确的角色以优化当前动作重构：它将解码得到的重构结果作为反馈，从剩余动作误差中构建下一个令牌，从而闭合编码与解码的循环，使每个新令牌对实际解码前缀留下的误差做出响应。在涵盖三个模拟基准和真实场景的19个操作任务中，采用8个令牌的ReAT策略在任务成功率上优于所有评估的基线；在LIBERO基准上，即使仅用4个令牌的ReAT，也能在令牌预算减半的情况下超越所有评估的8个令牌动作分词基线。这些结果为支持自校正策略学习的动作分词提供了可行路径，助力自回归策略逐步优化自身的动作规划。
英文摘要：
Autoregressive robot policies rely on action tokenizers to convert continuous action chunks into compact sequences of discrete prediction targets. Existing tokenizers can reconstruct action chunks from discrete codes, but often leave implicit what each individual token contributes to the reconstructed action. We introduce Residual Action Tokenization (ReAT), which gives each token an explicit role in refining the current action reconstruction. It uses the decoded reconstruction as feedback to construct the next token from the remaining action error. This closes the loop between encoding and decoding, making each new token responsive to the errors left by the actual decoded prefix. Across 19 manipulation tasks spanning three simulation benchmarks and real-world settings, 8-token ReAT policies outperform the evaluated baselines in task success; on LIBERO, even 4-token ReAT surpasses the evaluated 8-token action-tokenization baselines at half the token budget. These results suggest a path toward action tokenization that supports self-corrective policy learning, helping autoregressive policies learn to progressively refine their own action plans.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
