The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning

[Submitted on 6 May 2025 (v1), last revised 10 Jun 2026 (this version, v2)]

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Abstract: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 this https URL.

Submission history

From: Jan Ole von Hartz [view email]
[v1] Tue, 6 May 2025 08:27:23 UTC (25,380 KB)
[v2] Wed, 10 Jun 2026 08:58:09 UTC (27,159 KB)