无训练闭环扩散策略的模仿学习
Training-Free Imitation Learning with Closed-Form Diffusion Policies
AI总结：
提出一种基于演示数据集闭式得分的无训练扩散策略（CFDP），实现毫秒级实时模仿学习，性能媲美需数小时训练的神经基线，并支持推理时策略编辑与演示增强。
摘要：
尽管基于扩散的策略具有令人印象深刻的性能和表达能力，但其长时间离线训练拖慢了数据收集和策略部署循环。我们引入了闭环扩散策略（CFDP），这是一类使用从演示数据集导出的闭式得分的无训练扩散策略，用于模仿学习。我们在硬件实验中用移动CPU进行实时推理部署CFDP，表明它能够直接从数据集中毫秒级成功执行模仿，并且推理速度比神经扩散策略更快。在模仿学习基准实验中，我们展示了CFDP与需要数小时训练的神经基线相比具有竞争力，在训练时间和性能之间提供了有利的权衡。最后，我们展示了闭环扩散策略如何作为一种可组合原语，实现对预训练神经扩散策略的数据驱动推理时编辑，包括策略引导和新颖的演示增强。
英文摘要：
While diffusion-based policies have impressive performance and expressivity, their long offline training slows down the data collection and policy deployment loop. We introduce Closed-Form Diffusion Policies, a class of training-free diffusion-based policies for imitation learning using the closed-form score derived from the demonstration dataset. We deploy CFDP with real-time inference with a mobile CPU in hardware experiments, showing it can successfully perform imitation directly from the dataset in milliseconds and with faster inference than neural diffusion policies. In experiments on imitation learning benchmarks, we show that CFDP is competitive against neural baselines that require hours of training, providing a favorable tradeoff between training time and performance. Finally, we show how closed-form diffusion policies act as a composable primitive that enables data-driven inference-time editing of pre-trained neural diffusion policies, including policy guidance and novel demonstration augmentation.
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。
