This paper presents r, he complete integrated planning, executing and learning robotic agent ROGUE. We describe ROGUE’S task planner that interleaves high-level task planning with real world robot execution. It supports multiple, asynchronous goMs, suspends and interrupts tasks, mad monitors and compensates for failure. We present a general approach for learning situation-dependent rides from execution, which correlates environmental features with learning opportunities: thereby detecting patterns and allowing planners to predict and avoid falhlres. We present two implementations of the general learning approach, in the robot’s path planner, and in tim task planner. We present empirical data to show the effectiveness of RoGuE’s novel learning approach.
['Karen Zita Haigh', 'Manuela M. Veloso']
Planning, Execution and Learning in a Robotic Agent