X-Distill：跨架构视觉蒸馏用于视觉-运动学习
X-Distill: Cross-Architecture Vision Distillation for Visuomotor Learning
AI总结：
X-Distill通过跨架构知识蒸馏结合视觉转换器和紧凑型CNN，实现了在数据有限的机器人操作任务中优于其他方法的性能。
摘要：
视觉-运动策略通常利用大规模预训练的视觉转换器（ViTs）来利用其强大的泛化能力。然而，其显著的数据需求在大多数机器人学习环境中数据稀缺的背景下构成了重大挑战，其中紧凑型CNNs具有强大的归纳偏差可以更容易地进行优化。为了解决这一权衡，我们引入了X-Distill，一种简单但效果显著的方法，结合了两种架构的优势。我们的方法涉及一种离线的跨架构知识蒸馏，将大规模冻结的DINOv2教师的丰富视觉表示转移到紧凑型ResNet-18学生上，在通用的ImageNet数据集上进行。经过蒸馏的编码器现在拥有强大的视觉先验，然后与扩散策略头共同微调，以完成目标操作任务。在34个模拟基准和5个具有挑战性的现实任务上的广泛实验表明，我们的方法在数据高效机器人操作中 consistently 超过了配备从头开始的ResNet或微调DINOv2编码器的策略。值得注意的是，X-Distill还超过了利用特权点云观测或更大视觉-语言模型的3D编码器。我们的工作突显了简单但基础的蒸馏策略在实现数据高效机器人操作的最先进性能中的有效性。
英文摘要：
Visuomotor policies often leverage large pre-trained Vision Transformers (ViTs) for their powerful generalization capabilities. However, their significant data requirements present a major challenge in the data-scarce context of most robotic learning settings, where compact CNNs with strong inductive biases can be more easily optimized. To address this trade-off, we introduce X-Distill, a simple yet highly effective method that synergizes the strengths of both architectures. Our approach involves an offline, cross-architecture knowledge distillation, transferring the rich visual representations of a large, frozen DINOv2 teacher to a compact ResNet-18 student on the general-purpose ImageNet dataset. This distilled encoder, now endowed with powerful visual priors, is then jointly fine-tuned with a diffusion policy head on the target manipulation tasks. Extensive experiments on $34$ simulated benchmarks and $5$ challenging real-world tasks demonstrate that our method consistently outperforms policies equipped with from-scratch ResNet or fine-tuned DINOv2 encoders. Notably, X-Distill also surpasses 3D encoders that utilize privileged point cloud observations or much larger Vision-Language Models. Our work highlights the efficacy of a simple, well-founded distillation strategy for achieving state-of-the-art performance in data-efficient robotic manipulation.
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