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

arXiv: https://arxiv.org/abs/2306.13222 · HTML: https://mlsanity.com/paper/2306.13222/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [Specifying User Preferences using Weighted Signal Temporal Logic](https://mlsanity.com/paper/2010.00752/) (2020)
- [An Overview of Hierarchical Task Network Planning](https://mlsanity.com/paper/1403.7426/) (2014)
- [Let's Collaborate: Regret-based Reactive Synthesis for Robotic Manipulation](https://mlsanity.com/paper/2203.06861/) (2022)
- [Automata-based Optimal Planning with Relaxed Specifications](https://mlsanity.com/paper/2107.13650/) (2021)
- [Opportunistic Qualitative Planning in Stochastic Systems with Preferences over Temporal Logic Objectives](https://mlsanity.com/paper/2203.13803/) (2022)

## Built on by (most cited first, 2 of 2)

- [Preference-Based Planning in Stochastic Environments: From Partially-Ordered Temporal Goals to Most Preferred Policies](https://mlsanity.com/paper/2403.18212/) (2024)
- [Online Pareto-Optimal Decision-Making for Complex Tasks using Active Inference](https://mlsanity.com/paper/2406.11984/) (2024)

## Abstract (arXiv)

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.