ActAtlas: Learning Adaptive Execution Atlases for Long-Horizon Robotic Manipulation

Long-horizon robot manipulation requires a policy to perform a sequence of con- nected operations. Diffusion policies can replan from new observations, but they often fail when two states look similar yet belong to different task stages and there- fore need different actions. We argue that a policy should know not only which local pattern the current state belongs to, but also how this pattern is connected to the other patterns of the task. To this end, we propose ActAtlas, which learns a data-adaptive graph of task stages from demonstrations and uses it to condition a diffusion policy, so that the robot generates actions with knowledge of both the current stage and how it connects to the rest of the task. Specifically, in the first stage, ActAtlas learns a set of execution anchors from demonstrations with self- supervision. Each anchor is a prototype shared by similar states across demonstra- tions, and the number of anchors is adjusted during training by growing, merging, and splitting. Transitions between anchors form a directed execution-flow graph. In the second stage, a graph attention network combines the current anchor with related anchors into a fixed-size context vector, which conditions a single diffu- sion policy. Because the graph weights are further trained end-to-end with the action denoising loss, these two stages allow ActAtlas to recognize the current stage of the task and use the learned stage-to-stage relations to choose the correct next action. Across four simulation benchmarks, ActAtlas improves the average success rate by 12.4% over the strongest baseline, with the largest LIBERO gain (16.2%) on the long-horizon suite. On four real-world tasks, it improves the aver- age success rate by 25.0 %. More broadly, our results suggest that learning how task stages connect to each other, rather than only what the current state looks like, is a simple and effective way to make generative robot policies more reliable on long, multi-step tasks.