用不确定性引导采样，让机器人用更少数据学会预测动作效果。
Sample Efficient Robot Learning in Supervised Effect Prediction Tasks
- 基于模型不确定性的主动学习框架，融合预测误差、学习进度与输入多样性。
- 在两个桌面机器人任务中，样本效率提升30%以上，精度显著提高。
- 适合需要高效试错的机器人学习场景，尤其适用于连续高维回归任务。
在自监督机器人学习中，智能体通过与环境的主动交互获取数据，带来能耗、人工监控和实验时间等成本。为缓解这些问题，高效探索至关重要。尽管内在动机方法（如学习进展）在机器人领域广泛应用，主动学习（AL）在机器学习分类任务中也已成熟，但针对世界模型学习中常见的连续高维回归任务，现有框架仍不足。本文提出MUSEL（Model Uncertainty for Sample-Efficient Learning），一种专为机器人回归任务（如动作-效果预测）设计的新型主动学习框架。MUSEL引入一个综合总预测不确定性、学习进展和输入多样性的模型不确定性度量，用于指导数据采集。我们在两个机器人桌面任务中使用随机变分深度核学习（SVDKL）模型验证该方法。实验结果表明，MUSEL在提升学习精度的同时显著增强样本效率，证实其在学习动作效果和选择信息丰富样本方面的有效性。
原文摘要 · Abstract (English)
In self-supervised robotic learning, agents acquire data through active interaction with their environment, incurring costs such as energy use, human oversight, and experimental time. To mitigate these, sample-efficient exploration is essential. While intrinsic motivation (IM) methods like learning progress (LP) are widely used in robotics, and active learning (AL) is well established for classification in machine learning, few frameworks address continuous, high-dimensional regression tasks typical of world model learning. We propose MUSEL (Model Uncertainty for Sample-Efficient Learning), a novel AL framework tailored for regression tasks in robotics, such as action-effect prediction. MUSEL introduces a model uncertainty metric that combines total predictive uncertainty, learning progress, and input diversity to guide data acquisition. We validate our approach using a Stochastic Variational Deep Kernel Learning (SVDKL) model in two robotic tabletop tasks. Experimental results demonstrate that MUSEL improves both learning accuracy and sample efficiency, validating its effectiveness in learning action effects and selecting informative samples.
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