ConceptTree: Bringing Semantic Transparency to Black-Box Decision Making for Robotic Manipulation
2026-07-20 • Robotics
RoboticsArtificial Intelligence
AI summaryⓘ
The authors propose ConceptTree, a method for making robot decision-making in long tasks easier to understand by humans. Instead of using hidden code, their system uses clear, human-friendly concepts based on what the robot sees to decide what skill to use next. This creates a decision process that people can follow and even change directly if needed. They tested ConceptTree on real robot tasks and found it works better than similar methods, especially for complicated jobs. Their approach also allows fixing mistakes by adjusting concepts without having to retrain the robot.
robotic manipulationdecision-makingconcept learningdecision treevisual observationslong-horizon taskspolicy interpretabilityhuman oversightinterventionskill selection
Authors
Yongyan Wen, Feifan Liu, Jinyi Chen, Bo An, Peng Liu, Siyuan Li
Abstract
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.