Abstract
In this paper we propose a resumable interruption framework for robotic applications which allows to “filter” misrecognition signals after their occurrence. Handling misrecognition is essential for deploying reactive systems into the real world, since being over-reactive to detection errors can lead to livelocks and stagnation. For example, constantly interrupting and resuming a picking task due to misrecognition can make the robot alternate between pre-grasping and grasping motions, without ever achieving the task. Our solution is based on resumable interruptions, continuing interrupted procedures from the exact preemption point if similar execution requests are received shortly after a cancellation order. This acts as a post-facto misrecognition filter, which stabilizes execution and ensures task completion. Compared with standard filtering, the post-facto approach allows to deliver signals faster and recover from misrecognition longer. The proposed system is verified through real robot experiments in dynamic and static environments.
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de Campos Affonso, G., Okada, K., Inaba, M. (2023). Post-facto Misrecognition Filter Based on Resumable Interruptions for Coping with Real World Uncertainty in the Development of Reactive Robotic Behaviors. In: Petrovic, I., Menegatti, E., Marković, I. (eds) Intelligent Autonomous Systems 17. IAS 2022. Lecture Notes in Networks and Systems, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-031-22216-0_8
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