As robots become more physically robust and capable of sophisticated sensing, navigation,
and manipulation, we want them to carry out increasingly complex tasks. A robot that helps …

As robots become more physically robust and capable of sophisticated sensing, navigation,
and manipulation, we want them to carry out increasingly complex tasks. A robot that helps …

Real-world robotic environments are highly structured. The scalability of planning and
reasoning methods to cope with complex problems in such environments crucially depends …

The execution of long-horizon tasks under uncertainty is a fundamental challenge in
robotics. Recent approaches have made headway on these tasks with an integration of task …

Planning in real-world environments can be challenging for intelligent robots due to
incomplete domain knowledge that results from unpredictable domain dynamism, and due …

Complex robot tasks are usually described as high level goals, with no details on how to
achieve them. However, details must be provided to generate primitive commands to control …

To be useful in the real world, robots need to be able to move safely in unstructured
environments and achieve their given tasks despite unexpected environmental changes or …

Deployment of robots in practical domains poses key knowledge representation and
reasoning challenges. Robots need to represent and reason with incomplete domain …

Symbolic planning methods have proved to be challenging in robotics due to partial
observability and noise as well as unavoidable exceptions to rules that symbol semantics …

Making intelligent decisions is an essential capability of any robotic system. A reasoning
component that solves this problem must combine all available skills of a robot to solve a …