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 in imitation learning 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.

翻译：模仿学习已使机器人能够从RGB观测中实现高度灵巧的操作。然而，通过此类方法训练的策略通常仅基于短期观测历史来调节机器人动作。这些策略无法解决需要记忆的任务，并且容易反复执行相同的失败动作。在本工作中，我们首先在多个数据机制下，针对一系列具有不同局部稳定性和记忆需求的任务，逐步将上下文长度从短至长进行增量增加，从而基准测试策略性能。据我们所知，这是首个在如此细致的层面探究模仿学习中上下文长度的研究。我们的结果挑战了先前的观点：朴素地扩展上下文长度并不像文献中宣称的那样脆弱。通过适当的调节方法和去噪骨干网络（UNet+交叉注意力），单任务策略即使在朴素扩展的情况下，也能在常规数据机制下的许多任务中实现高成功率。接下来，我们提出一种训练算法，用于联合训练多种上下文长度的策略，进一步降低长上下文学习的样本复杂度。最后，我们应用研究结果重新评估了先前提出的长上下文模仿学习解决方案。