Intelligent Execution through Plan Analysis
This addresses the challenge of robust plan execution for intelligent robots in complex real-world environments, though it appears incremental as it builds on existing planning and monitoring methods.
The paper tackles the problem of robots dealing with unfulfilled assumptions during plan execution by focusing on positive opportunities for better plans, rather than just replanning after failures, and shows that the approach outperforms standard replanning strategies in robotic tasks.
Intelligent robots need to generate and execute plans. In order to deal with the complexity of real environments, planning makes some assumptions about the world. When executing plans, the assumptions are usually not met. Most works have focused on the negative impact of this fact and the use of replanning after execution failures. Instead, we focus on the positive impact, or opportunities to find better plans. When planning, the proposed technique finds and stores those opportunities. Later, during execution, the monitoring system can use them to focus perception and repair the plan, instead of replanning from scratch. Experiments in several paradigmatic robotic tasks show how the approach outperforms standard replanning strategies.