Optimal Cost-Preference Trade-off Planning with Multiple Temporal Tasks

TL;DR

This paper introduces a novel framework for preference-based planning in autonomous robots, enabling Pareto-optimal trade-offs between user preferences and resource efficiency through an extended A* search method.

Contribution

It extends preference notions to individual tasks, develops a Pareto analysis framework, and proposes scalable algorithms for generating optimal trade-offs in robot planning.

Findings

Efficient Pareto front computation via multi-objective A*

Significant speedup with the proposed heuristic (up to 100x)

Effective planning demonstrated on mobile robots and manipulators

Abstract

Autonomous robots are increasingly utilized in realistic scenarios with multiple complex tasks. In these scenarios, there may be a preferred way of completing all of the given tasks, but it is often in conflict with optimal execution. Recent work studies preference-based planning, however, they have yet to extend the notion of preference to the behavior of the robot with respect to each task. In this work, we introduce a novel notion of preference that provides a generalized framework to express preferences over individual tasks as well as their relations. Then, we perform an optimal trade-off (Pareto) analysis between behaviors that adhere to the user's preference and the ones that are resource optimal. We introduce an efficient planning framework that generates Pareto-optimal plans given user's preference by extending A* search. Further, we show a method of computing the entire Pareto front (the set of all optimal trade-offs) via an adaptation of a multi-objective A* algorithm. We also present a problem-agnostic search heuristic to enable scalability. We illustrate the power of the framework on both mobile robots and manipulators. Our benchmarks show the effectiveness of the heuristic with up to 2-orders of magnitude speedup.