POrTAL: Plan-Orchestrated Tree Assembly for Lookahead
This work addresses planning inefficiencies for robots with limited computational resources, though it appears incremental as it builds on existing baseline algorithms.
The paper tackles the problem of inefficient probabilistic planning for robots in partially observable environments by introducing POrTAL, a lightweight algorithm that combines FF-Replan and POMCP, resulting in shorter plan lengths under bounded computation time, especially in moderately uncertain scenarios.
When tasking robots in partially observable environments, these robots must efficiently and robustly plan to achieve task goals under uncertainty. Although many probabilistic planning algorithms exist for this purpose, these algorithms can be inefficient if executed with the robot's limited computational resources, or may produce policies that take more steps than expected to achieve the goal. We therefore created a new, lightweight, probabilistic planning algorithm, Plan-Orchestrated Tree Assembly for Lookahead (POrTAL), that combines the strengths of two baseline planning algorithms, FF-Replan and POMCP. We demonstrate that POrTAL is an anytime algorithm that generally outperforms these baselines in terms of the final executed plan length given bounded computation time, especially for problems with only moderate levels of uncertainty.