发表机构
Oregon State University（俄勒冈州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对现有动作模型学习方法难以处理条件与量化效果的问题，提出OHCAM方法，通过不确定性引导探索学习动作模型，在基准领域与机器人任务中验证其样本效率与现实适用性。
AI 中文摘要
准确的动作模型对于高效规划至关重要。现有动作模型学习方法大多假设动作表示简单，或在学习条件和量化效果时计算上变得难以处理。我们提出在线假设驱动的条件动作模型学习（OHCAM），这是一种从与环境的有限交互中学习具有条件和量化效果的动作模型的在线方法。OHCAM维护对假设动作模型的置信度，并通过最大化竞争假设之间的分歧来主动选择信息丰富的动作以减少不确定性，同时对噪声观测具有鲁棒性。为实现可扩展性，OHCAM从一小部分简单动作模型假设开始，仅在当前假设与数据不一致时才扩展到更复杂的条件。在六个基准规划领域上的实验表明，OHCAM在学习动作模型方面具有样本效率，这些动作模型比基线能解决多得多的任务，即使存在观测噪声。我们在使用Kinova Gen3机器人的两项任务上验证了OHCAM，证明了我们方法在现实世界中的适用性。
英文摘要
Accurate action models are critical for effective planning. Existing action-model learning methods largely assume simple action representations or become computationally intractable when learning conditional and quantified effects. We present Online Hypothesis-Driven Conditional Action Model Learning (OHCAM), an online approach for learning action models with conditional and quantified effects from limited interactions with the environment. OHCAM maintains a belief over hypothesized action models and actively selects informative actions to reduce uncertainty by maximizing disagreement among competing hypotheses, while being robust to noisy observations. To enable scalability, OHCAM begins with a small set of simple action model hypotheses and expands to more complex conditions only when the current hypotheses become inconsistent with the data. Experiments on six benchmark planning domains demonstrate that OHCAM is sample efficient in learning action models that solve substantially more tasks than baselines, even with observation noise. We validate OHCAM on two tasks using a Kinova Gen3 robot, demonstrating the real-world applicability of our approach.
