We present Mixture of Discrete-time Gaussian Processes (MiDiGap), a novel approach for flexible policy representation and imitation learning in robot manipulation. MiDiGap enables learning from as few as five demonstrations using only camera observations and generalizes across a wide range of challenging tasks. It excels at long-horizon behaviors such as making coffee, highly constrained motions such as opening doors, dynamic actions such as scooping with a spatula, and multimodal tasks such as hanging a mug. MiDiGap learns these tasks on a CPU in less than a minute and scales linearly to large datasets. We also develop a rich suite of tools for inference-time steering using evidence such as collision signals and robot kinematic constraints. This steering enables novel generalization capabilities, including obstacle avoidance and cross-embodiment policy transfer. MiDiGap achieves state-of-the-art performance on diverse few-shot manipulation benchmarks. On constrained RLBench tasks, it improves policy success by 76 percentage points and reduces trajectory cost by 67%. On multimodal tasks, it improves policy success by 48 percentage points and increases sample efficiency by a factor of 20. In cross-embodiment transfer, it more than doubles policy success. We make the code publicly available at https://midigap.cs.uni-freiburg.de.

翻译：我们提出离散时间高斯过程混合模型（MiDiGap），一种用于机器人操作中灵活策略表示与模仿学习的新方法。MiDiGap仅需利用摄像头观测从最少五次示范中学习，并能泛化至广泛挑战性任务。该模型擅长处理长期行为（如制作咖啡）、高度约束动作（如开门）、动态操作（如用铲子舀取）以及多模态任务（如挂杯子）。MiDiGap在CPU上学习这些任务耗时不到一分钟，且能线性扩展至大规模数据集。我们还开发了一套丰富的推理时引导工具，利用碰撞信号和机器人运动学约束等证据进行干预。这种引导实现了包括避障和跨本体策略迁移在内的新型泛化能力。MiDiGap在多种少样本操作基准测试中达到了最先进性能。在受约束的RLBench任务中，策略成功率提升76个百分点，轨迹代价降低67%；在多模态任务中，策略成功率提升48个百分点，样本效率提高20倍；在跨本体迁移中，策略成功率提升超过一倍。我们已在https://midigap.cs.uni-freiburg.de公开代码。