提升扩散策略的上下文长度，让机器人记住更长时间的视觉信息来完成复杂任务。
Training and Evaluating Diffusion Policies with Long Context Lengths
- 用跨注意力机制在扩散模型中扩展观察历史，实现长时序条件建模。
- 简单扩展上下文长度也能在多数任务上获得高成功率，无需复杂修改。
- 提出多上下文联合训练算法，显著降低长时记忆学习的数据需求。
模仿学习已使机器人能从RGB图像实现高灵巧性操作，但现有策略通常仅依赖短期观察历史，难以处理需记忆的任务，常陷入重复失败动作。本文首次系统地评估了从短到长的上下文长度对扩散策略性能的影响，覆盖多种具有不同局部稳定性与记忆需求的任务及数据场景。结果表明，简单扩展上下文长度并不如文献所言般脆弱；采用合适的条件化方法与去噪主干（UNet+Cross-Attention），单任务策略在常规数据下即可在多数任务中达到高成功率。随后提出一种多上下文长度联合训练算法，进一步降低长时记忆学习的样本复杂度。最后，基于上述发现重新评估了若干已有长上下文模仿学习方案的有效性。
原文摘要 · Abstract (English)
Imitation learning has enabled highly-dexterous robotic manipulation from RGB observations. Policies trained with these methods, however, typically condition robot actions on only a short history of observations. These policies cannot solve tasks that require memory and can get stuck repeatedly executing the same failing motions. In this work, we first benchmark policy performance as context length is incrementally increased from short to long, across a spectrum of tasks with varying local stability and memory requirements, and in multiple data regimes. To our knowledge, this is the first study to investigate context length for Diffusion Policies at this level of detail. Our results challenge prior claims: naively scaling context length is not as brittle as advertised in literature. With an appropriate conditioning method and denoising backbone (UNet+Cross-Attention), single-task policies achieve high success rates on many tasks in the usual data regime even with naive scaling. Next, we propose a training algorithm to jointly train policies at multiple context lengths, further reducing the sample complexity of long-context learning. Finally, we apply our findings to re-evaluate some previously proposed solutions to long-context imitation learning.
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