A Robust Planning Framework for Cognitive Robots

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 encountered 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 framework combines planning, reasoning and learning procedures into each other for robust execution of cognitive tasks. Failures can be detected and handled by reasoning and replanning, 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.