A Robust Planning Framework for Cognitive Robots
Sertaç Karapınar, Doğan Altan, Sanem Sarıel · 2012
A cognitive robot should construct a plan to attain its goals. While it executes the actions in its plan, it may face several failures due to both internal and external issues. We present a taxonomy to classify these failures that may be encoun-tered during the execution of cognitive tasks. The taxonomy presents a wide range of failure types. To recover from most of these failures presented in this taxonomy, we propose a Robust Planning Framework for cognitive robots. Our frame-work combines planning, reasoning and learning procedures into each other for robust execution of cognitive tasks. Fail-ures can be detected and handled by reasoning and replan-ning, respectively. The framework also facilitates learning new hypotheses incrementally based on experience. It can successfully detect and recover from temporary failures on a selected set of actions executed by a Pioneer3DX robot. It has been shown that our preliminary results for hypothesis learning in failure scenarios are promising.