A common failure mode for policies trained with imitation is compounding
execution errors at test time. When the learned policy encounters states that
were not present in the expert demonstrations, the policy fails, leading to
degenerate behavior. The Dataset Aggregation, or DAgger approach to this
problem simply collects more data to cover these failure states. However, in
practice, this is often prohibitively expensive. In this work, we propose
Diffusion Meets DAgger (DMD), a method to reap the benefits of DAgger without
the cost for eye-in-hand imitation learning problems. Instead of collecting new
samples to cover out-of-distribution states, DMD uses recent advances in
diffusion models to create these samples with diffusion models. This leads to
robust performance from few demonstrations. In experiments conducted for
non-prehensile pushing on a Franka Research 3, we show that DMD can achieve a
success rate of 80% with as few as 8 expert demonstrations, where naive
behavior cloning reaches only 20%. DMD also outperform competing NeRF-based
augmentation schemes by 50%.

为了解决在执行过程中模拟训练策略中的错误累积问题，本研究提出了一种不需要昂贵代价的 Diffusion Meets DAgger（DMD）方法，利用扩散模型生成能够覆盖分布外状态的样本，从而实现从少量示范中获得稳健性能，实验证明 DMD 比基于 NeRF 的增强方案提高了 50% 的性能。