Spatial Concept Bottleneck for Interpretable Robot Learning

Robot learning models have been proliferating with increasingly stronger results for both generalist and specialist policies. However, true deployment potential of these learned policies is still unknown. As the community closes in on translational research, it is now ever more crucial to develop mechanisms that not only enable interpretability but intervention as well, e.g., through concept bottleneck models. In this work, we present an initial investigation along the lines of an interpretable spatial concept bottleneck that acts as a meaningful intermediate representation between a perception-planning backbone and an action head. Specifically, we define a spatial concept as a geometric relationship between entities corresponding to the task and the robot, which can be computed directly from the scene's geometry, and instantiate it for both navigation and manipulation. We present a series of experiments demonstrating that such a spatial concept bottleneck maintains original performance (without the bottleneck), while serving as an interpretable interface through which humans or VLMs can intervene to correct or steer the robot. Videos and results are available at https://spatial-cbm.github.io.