ConceptTree：为机器人操作的黑箱决策带来语义透明度

中文摘要 AI 辅助

在长期的机器人操作中建立可解释的决策过程对于实现可靠的人类监督和干预至关重要。然而，现有的机器人操作方法大多将技能选择视为从观察到行动的不透明映射，对决策形成方式的透明度有限。在这项工作中，我们提出了ConceptTree，一个将高级操作技能选择重新构建为对人类可解释概念进行推理的框架，将高级策略表示为基于视觉观察的一系列概念级谓词。我们的方法不是依赖于隐式潜在表示，而是学习一个基于视觉输入的归一化概念空间，在其上训练决策树以预测高级技能。这种表述产生了一个可追溯和可干预的透明决策过程，能够直接检查和修改策略行为。我们在一组复杂度不断增加的实际机器人操作任务上评估了我们的方法。实验结果表明，ConceptTree始终优于现有的基于概念的基线，特别是在复杂的长期场景中。此外，我们提供的定性案例研究表明，我们的模型通过修改单个概念支持细粒度干预，能够在不重新训练的情况下有针对性地纠正决策错误。

英文摘要

Establishing interpretable decision-making processes in long-horizon robotic manipulation is critical for enabling reliable human oversight and intervention. However, existing approaches to robotic manipulation largely treat skill selection as opaque mappings from observations to actions, offering limited transparency into how decisions are formed. In this work, we propose ConceptTree, a framework that reframes high-level manipulation skill selection as reasoning over human-interpretable concepts, representing high-level policies as a sequence of concept-level predicates over visual observations. Rather than relying on implicit latent representations, our method learns a normalized concept space grounded in visual inputs, over which a decision tree is trained to predict high-level skills. This formulation yields a transparent decision process that is both traceable and intervenable, enabling direct inspection and modification of policy behavior. We evaluate our approach on a set of real-world robotic manipulation tasks with increasing complexity. Experimental results show that ConceptTree consistently outperforms existing concept-based baselines, particularly in complex, long-horizon scenarios. Furthermore, we provide qualitative case studies showing that our model supports fine-grained intervention by modifying individual concepts, enabling targeted correction of decision errors without retraining.

发表机构

• Harbin Institute of Technology（哈尔滨工业大学）
• Nanyang Technological University（南洋理工大学）

机构由 AI 辅助整理，请以论文原文为准。