用逻辑概率模型让机器人持续学习新物品功能，不重训也能用。
Incremental Learning of Affordances using Markov Logic Networks
- 基于马尔可夫逻辑网络，仅更新变化部分知识，避免全量重训练。
- 在未见物体上实现零样本功能推断，性能优于主流基线方法。
- 适合需要长期积累环境知识的移动机器人场景。
物体功能（affordances）使机器人能够语义理解周围环境，从而提升任务执行灵活性。由于功能依赖上下文，机器学习建模极具挑战。马尔可夫逻辑网络（MLN）结合概率推理与逻辑表达，能有效捕捉上下文信息。移动机器人在部分已知环境中运行，会遇到未知物体的功能。这些新信息需被纳入已有知识体系，而无需从头重新训练整个模型。本文提出MLN累积学习算法（MLN-CLA），通过保留已有知识并仅更新变动部分，实现增量式学习。实验表明，该方法在累积学习和零样本功能推断任务中表现优异，显著超越多个强基线模型。
原文摘要 · Abstract (English)
Affordances enable robots to have a semantic understanding of their surroundings. This allows them to have more acting flexibility when completing a given task. Capturing object affordances in a machine learning model is a difficult task, because of their dependence on contextual information. Markov Logic Networks (MLN) combine probabilistic reasoning with logic that is able to capture such context. Mobile robots operate in partially known environments wherein unseen object affordances can be observed. This new information must be incorporated into the existing knowledge, without having to retrain the MLN from scratch. We introduce the MLN Cumulative Learning Algorithm (MLN-CLA). MLN-CLA learns new relations in various knowledge domains by retaining knowledge and only updating the changed knowledge, for which the MLN is retrained. We show that MLN-CLA is effective for accumulative learning and zero-shot affordance inference, outperforming strong baselines.
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