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Robots are increasingly entering uncertain and unstructured environments.Within these, robots are bound to face unexpected external disturbances likeaccidental human or tool collisions. Robots must develop the capacity torespond to unexpected events. That is not only identifying the sudden anomaly,but also deciding how to handle it. In this work, we contribute a recoverypolicy that allows a robot to recovery from various anomalous scenarios acrossdifferent tasks and conditions in a consistent and robust fashion. The systemorganizes tasks as a sequence of nodes composed of internal modules such asmotion generation and introspection. When an introspection module flags ananomaly, the recovery strategy is triggered and reverts the task execution byselecting a target node as a function of a state dependency chart. The newskill allows the robot to overcome the effects of the external disturbance andconclude the task. Our system recovers from accidental human and toolcollisions in a number of tasks. Of particular importance is the fact that wetest the robustness of the recovery system by triggering anomalies at each nodein the task graph showing robust recovery everywhere in the task. We alsotrigger multiple and repeated anomalies at each of the nodes of the taskshowing that the recovery system can consistently recover anywhere in thepresence of strong and pervasive anomalous conditions. Robust recovery systemswill be key enablers for long-term autonomy in robot systems. Supplemental infoincluding code, data, graphs, and result analysis can be found at [1].

Recovering from External Disturbances in Online Manipulation through State-Dependent Revertive Recovery Policies