Adaptive Human-Robot Collaborative Missions using Hybrid Task Planning

Gricel Vazquez Flores; Alexandros Evangelidis; Sepeedeh Shahbeigi Roudposhti; Simos Gerasimou

Adaptive Human-Robot Collaborative Missions using Hybrid Task Planning

Gricel Vazquez Flores, Alexandros Evangelidis, Sepeedeh Shahbeigi Roudposhti, Simos Gerasimou

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Producing robust task plans in human-robot collaborative missions is a critical activity in order to increase the likelihood of these missions completing successfully. Despite the broad research body in the area, which considers different classes of constraints and uncertainties, its applicability is confined to relatively simple problems that can be comfortably addressed by the underpinning mathematically-based or heuristic-driven solver engines. In this paper, we introduce a hybrid approach that effectively solves the task planning problem by decomposing it into two intertwined parts, starting with the identification of a feasible plan and followed by its uncertainty augmentation and verification yielding a set of Pareto optimal plans. To enhance its robustness, adaptation tactics are devised for the evolving system requirements and agents’ capabilities. We demonstrate our approach through an industrial case study involving workers and robots undertaking activities within a vineyard, showcasing the benefits of our hybrid approach both in the generation of feasible solutions and scalability compared to native planners.

Original language	English
Title of host publication	20th International Conference on Software Engineering for Adaptive and Self-Managing Systems
Publication status	Accepted/In press - 11 Feb 2025
Event	20th International Conference on Software Engineering for Adaptive and Self-Managing Systems - Ottawa, Canada Duration: 28 Apr 2025 → 29 Apr 2025

Conference

Conference	20th International Conference on Software Engineering for Adaptive and Self-Managing Systems
Country/Territory	Canada
City	Ottawa
Period	28/04/25 → 29/04/25

Bibliographical note

This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.

Embargoed Document

SEAMS_Task_planning_under_uncertainty
Accepted author manuscript, 2.86 MB
Licence: CC BY
Embargo ends: 29/04/25

Cite this

@inproceedings{4d8e4e5f4a8042358a5dfe595f6e4dcf,

title = "Adaptive Human-Robot Collaborative Missions using Hybrid Task Planning",

abstract = "Producing robust task plans in human-robot collaborative missions is a critical activity in order to increase the likelihood of these missions completing successfully. Despite the broad research body in the area, which considers different classes of constraints and uncertainties, its applicability is confined to relatively simple problems that can be comfortably addressed by the underpinning mathematically-based or heuristic-driven solver engines. In this paper, we introduce a hybrid approach that effectively solves the task planning problem by decomposing it into two intertwined parts, starting with the identification of a feasible plan and followed by its uncertainty augmentation and verification yielding a set of Pareto optimal plans. To enhance its robustness, adaptation tactics are devised for the evolving system requirements and agents{\textquoteright} capabilities. We demonstrate our approach through an industrial case study involving workers and robots undertaking activities within a vineyard, showcasing the benefits of our hybrid approach both in the generation of feasible solutions and scalability compared to native planners. ",

author = "{Vazquez Flores}, Gricel and Alexandros Evangelidis and {Shahbeigi Roudposhti}, Sepeedeh and Simos Gerasimou",

note = "This is an author-produced version of the published paper. Uploaded in accordance with the University{\textquoteright}s Research Publications and Open Access policy. ; 20th International Conference on Software Engineering for Adaptive and Self-Managing Systems ; Conference date: 28-04-2025 Through 29-04-2025",

year = "2025",

month = feb,

day = "11",

language = "English",

booktitle = "20th International Conference on Software Engineering for Adaptive and Self-Managing Systems",

}

TY - GEN

T1 - Adaptive Human-Robot Collaborative Missions using Hybrid Task Planning

AU - Vazquez Flores, Gricel

AU - Evangelidis, Alexandros

AU - Shahbeigi Roudposhti, Sepeedeh

AU - Gerasimou, Simos

N1 - This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.

PY - 2025/2/11

Y1 - 2025/2/11

N2 - Producing robust task plans in human-robot collaborative missions is a critical activity in order to increase the likelihood of these missions completing successfully. Despite the broad research body in the area, which considers different classes of constraints and uncertainties, its applicability is confined to relatively simple problems that can be comfortably addressed by the underpinning mathematically-based or heuristic-driven solver engines. In this paper, we introduce a hybrid approach that effectively solves the task planning problem by decomposing it into two intertwined parts, starting with the identification of a feasible plan and followed by its uncertainty augmentation and verification yielding a set of Pareto optimal plans. To enhance its robustness, adaptation tactics are devised for the evolving system requirements and agents’ capabilities. We demonstrate our approach through an industrial case study involving workers and robots undertaking activities within a vineyard, showcasing the benefits of our hybrid approach both in the generation of feasible solutions and scalability compared to native planners.

AB - Producing robust task plans in human-robot collaborative missions is a critical activity in order to increase the likelihood of these missions completing successfully. Despite the broad research body in the area, which considers different classes of constraints and uncertainties, its applicability is confined to relatively simple problems that can be comfortably addressed by the underpinning mathematically-based or heuristic-driven solver engines. In this paper, we introduce a hybrid approach that effectively solves the task planning problem by decomposing it into two intertwined parts, starting with the identification of a feasible plan and followed by its uncertainty augmentation and verification yielding a set of Pareto optimal plans. To enhance its robustness, adaptation tactics are devised for the evolving system requirements and agents’ capabilities. We demonstrate our approach through an industrial case study involving workers and robots undertaking activities within a vineyard, showcasing the benefits of our hybrid approach both in the generation of feasible solutions and scalability compared to native planners.

M3 - Conference contribution

BT - 20th International Conference on Software Engineering for Adaptive and Self-Managing Systems

T2 - 20th International Conference on Software Engineering for Adaptive and Self-Managing Systems

Y2 - 28 April 2025 through 29 April 2025

ER -