# Trust-based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop

arXiv: https://arxiv.org/abs/1808.05120 · HTML: https://mlsanity.com/paper/1808.05120/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [Trust-Oriented Adaptive Guardrails for Large Language Models](https://mlsanity.com/paper/2408.08959/) (2024)
- [Trust-Aware Planning: Modeling Trust Evolution in Iterated Human-Robot Interaction](https://mlsanity.com/paper/2105.01220/) (2021)
- [A Mental Model Based Framework of Trust](https://mlsanity.com/paper/2301.12569/) (2023)
- [Utility Theory based Cognitive Modeling in the Application of Robotics: A Survey](https://mlsanity.com/paper/2306.09445/) (2023)

## Abstract (arXiv)

Symbolic motion planning for robots is the process of specifying and planning robot tasks in a discrete space, then carrying them out in a continuous space in a manner that preserves the discrete-level task specifications. Despite progress in symbolic motion planning, many challenges remain, including addressing scalability for multi-robot systems and improving solutions by incorporating human intelligence. In this paper, distributed symbolic motion planning for multi-robot systems is developed to address scalability. More specifically, compositional reasoning approaches are developed to decompose the global planning problem, and atomic propositions for observation, communication, and control are proposed to address inter-robot collision avoidance. To improve solution quality and adaptability, a dynamic, quantitative, and probabilistic human-to-robot trust model is developed to aid this decomposition. Furthermore, a trust-based real-time switching framework is proposed to switch between autonomous and manual motion planning for tradeoffs between task safety and efficiency. Deadlock- and livelock-free algorithms are designed to guarantee reachability of goals with a human-in-the-loop. A set of non-trivial multi-robot simulations with direct human input and trust evaluation are provided demonstrating the successful implementation of the trust-based multi-robot symbolic motion planning methods.