用合成数据训练机器人仅凭一次示范就学会新任务，无需真实世界数据。
SynthICL: Scalable In-context Imitation Learning with Synthetic Data
- 全用合成RGB图像生成训练数据，不依赖深度传感器或真实数据
- 仅用一个示范在16个真实任务上平均成功率79%，超越以往方法
- 通过预测子目标图像实现更精准的视觉控制，适合快速部署到新任务
上下文模仿学习（ICIL）使机器人仅通过少量示范就能在测试时通过条件化预训练策略学习新任务，而无需重新训练。尽管前景广阔，但训练通用且可扩展的上下文模仿策略仍面临挑战。本文提出SynthICL，一种完全基于仅含RGB的合成数据训练的可扩展框架。我们构建了一个高保真度的ICIL数据生成流水线，并在此数据集上训练了一个流匹配变压器策略。SynthICL避免了先前方法中对深度感知、精确相机标定和真实世界训练数据的需求，提供了一种更简单、更具可扩展性的替代方案。我们进一步引入子目标预测，训练模型预测下一个子目标图像，从而实现更精确、以视觉为基础的控制。在16个未见过的真实世界操作任务上评估，仅提供一次示范时，平均成功率达79%，优于已有方法。项目页面：https://synth-icl.github.io
原文摘要 · Abstract (English)
In-context imitation learning (ICIL) enables robots to learn new tasks from a small number of demonstrations by conditioning a pre-trained policy on task-specific examples, without retraining at test time. Despite this promise, training generalizable and scalable in-context imitation policies remains an open challenge. We present SynthICL, a scalable framework that trains ICIL policies entirely from RGB-only synthetic data. Specifically, we build a data generation pipeline to produce high-fidelity ICIL data and train a flow-matching transformer policy on the resulting dataset. SynthICL avoids the need for depth sensing, precise camera calibration, and real-world training data in prior approaches, offering a simpler and more scalable alternative. We further incorporate subgoal prediction by training the model to predict the next subgoal images, enabling more precise and visually grounded control. Evaluated on 16 unseen real-world manipulation tasks, SynthICL achieves an average success rate of 79% with only one demonstration provided at test time and outperforms prior methods. Project page: https://synth-icl.github.io
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