Sequential Planning via Anchored Robotic Keypoints

Changes in a robot’s observations do not automatically imply changes in the task it is solving, but language-conditioned manipulation systems often fail to separate the two. If an object moves, for example, the locations at which actions should be executed may change while the underlying manipulation plan remains valid. We introduce Sequential Planning via Anchored Robotic Keypoints (SPARK), which separates a task's symbolic plan from its execution-time perceptual grounding, and we experimentally isolate whether a fixed symbolic plan can survive substantial changes to the scene through execution-time re-grounding. SPARK represents the task as a symbolic behavior tree whose object references are grounded to current 3D observations during execution, allowing the robot to update where actions are performed while preserving the plan. Runtime verification then enables local recovery and renewed grounding, only resorting to a new plan if necessary. To test the separation between plan and perceptual-grounding, we perform a set of manipulation experiments with a fixed plan while moving objects during execution. Under a 10 cm displacement, verification and adaptation maintain 66.7% task success, compared with 2.2% when disabled, demonstrating the importance of the underlying principle. Without task-specific model training, SPARK also achieves 72.6% mean success across six LIBERO-PRO settings and 68% across eleven physical task–embodiment settings on three robot platforms. Together, these results show that robustness does not require re-planning the task and for an important class of failures, it is sufficient to preserve the plan and update the system's perception of a changing world. Code and videos can be found at https://longjohnsilvers22.github.io/spark_page/