Publications

Conference Papers

Modeling and Optimizing the Provisioning of Exhaustible Capabilities for Simultaneous Task Allocation and Scheduling

Published in International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026), 2026

This work has been accepted as a full paper.

Recommended citation: Jinwoo Park, Harish Ravichandar, and Seth Hutchinson. 2026. Modeling and Optimizing the Provisioning of Exhaustible Capabilities for Simultaneous Task Allocation and Scheduling. In Proc. of the 25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026), Paphos,Cyprus, May 25 – 29, 2026, IFAAMAS, 9 pages.
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Risk-Tolerant Task Allocation and Scheduling in Heterogeneous Multi-Robot Teams

Published in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023

Effective coordination of heterogeneous multi-robot teams requires optimizing allocations, schedules, and motion plans in order to satisfy complex multi-dimensional task requirements. This challenge is exacerbated by the fact that real-world applications inevitably introduce uncertainties into robot capabilities and task requirements. In this paper, we extend our previous work on trait-based time-extended task allocation to account for such uncertainties. Specifically, we leverage the Sequential Probability Ratio Test to develop an algorithm that can guarantee that the probability of failing to satisfy task requirements is below a user-specified threshold. We also improve upon our prior approach by accounting for temporal deadlines in addition to synchronization and precedence constraints in a Mixed-Integer Linear Programming model. We evaluate our approach by benchmarking it against three baselines in a simulated battle domain in a city environment and compare its performance against a state-of-the-art framework in a pandemic-inspired multi-robot service coordination problem. Results demonstrate the effectiveness and advantages of our approach, which leverages redundancies to manage risk while simultaneously minimizing makespan.

Recommended citation: J. Park, A. Messing, H. Ravichandar and S. Hutchinson, "Risk-Tolerant Task Allocation and Scheduling in Heterogeneous Multi-Robot Teams," 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA, 2023, pp. 5372-5379, doi: 10.1109/IROS55552.2023.10341837.
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Preprints

Learning and Optimizing the Efficacy of Spatio-Temporal Task Allocation under Temporal and Resource Constraints

Published in arXiv, 2026

Complex multi-robot missions often require heterogeneous teams to jointly optimize task allocation, scheduling, and path planning to improve team performance under strict constraints. We formalize these complexities into a new class of problems, dubbed Spatio-Temporal Efficacy-optimized Allocation for Multi-robot systems (STEAM). STEAM builds upon trait-based frameworks that model robots using their capabilities (e.g., payload and speed), but goes beyond the typical binary success-failure model by explicitly modeling the efficacy of allocations as trait-efficacy maps. These maps encode how the aggregated capabilities assigned to a task determine performance. Further, STEAM accommodates spatio-temporal constraints, including a user-specified time budget (i.e., maximum makespan). To solve STEAM problems, we contribute a novel algorithm named Efficacy-optimized Incremental Task Allocation Graph Search (E-ITAGS) that simultaneously optimizes task performance and respects time budgets by interleaving task allocation, scheduling, and path planning. Motivated by the fact that trait-efficacy maps are difficult, if not impossible, to specify, E-ITAGS efficiently learns them using a realizability-aware active learning module. Our approach is realizability-aware since it explicitly accounts for the fact that not all combinations of traits are realizable by the robots available during learning. Further, we derive experimentally-validated bounds on E-ITAGS’ suboptimality with respect to efficacy. Detailed numerical simulations and experiments using an emergency response domain demonstrate that E-ITAGS generates allocations of higher efficacy compared to baselines, while respecting resource and spatio-temporal constraints. We also show that our active learning approach is sample efficient and establishes a principled tradeoff between data and computational efficiency.

Recommended citation: Jiazhen Liu, Glen Neville, Jinwoo Park, Sonia Chernova, and Harish Ravichandar. "Learning and Optimizing the Efficacy of Spatio-Temporal Task Allocation under Temporal and Resource Constraints." arXiv preprint arXiv:2601.02505 (2026).
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Dissertations

Multi-Objective Task Allocation and Scheduling for Heterogeneous Multi-Robot Systems

Published in Georgia Institute of Technology, 2025

Robotics Ph.D. Dissertation.

Recommended citation: Jinwoo Park, Multi-Objective Task Allocation and Scheduling for Heterogeneous Multi-Robot Systems, Doctoral Dissertation, Georgia Institute of Technology, 2025.

Jinwoo Park (박진우)