Changes to what a robot can see do not necessarily mean that there is a change in the problem it is trying to solve, and many language-conditioned manipulation systems struggle with this distinction. When something in a scene moves, the robot might need to execute its actions in different places, but its manipulation plan can stay the same. We propose Sequential Planning via Anchored Robotic Keypoints (\spark), which describes a task as a symbolic behavior tree that we only anchor to the current 3D observations when each action is called. By verifying the plan at runtime we can recover from failures locally and anchor the objects to the scene again. We ask for a new plan only if this fails. We isolate this concept by taking a fixed plan and moving objects in the scene while executing. When displacing objects by $10$\,cm we were still able to complete $66.7\%$ of trials with scene-diff verification and layered recovery, and we did not have to change the plan. We test our full system on \libpro with these conditions. There is no practice on the tasks being tested and no access to simulator states during rollouts, and as in \rats and \capx a success check after each attempt decides whether to retry. We use one open-weight 27B planner with 4-bit weights. Under this protocol \spark achieves a mean success of $66.8\%$ across the 6 position and task settings and $66.8\%$ on the held-out language and object settings. We also achieve a $68\%$ success rate across 11 physical task-embodiment settings on 3 different robots. Our work demonstrates that for an important class of failures the robot does not need to ask for a new plan for its task. It can be enough to update the system's view of the world. Code and videos can be found at https://cwru-aism.github.io/spark-page/

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