{"type":"ScholarlyArticle","title":"Real-Time Multi-Robot Motion Planning with Safe-Interval Search and Learning-Guided Repair","authors":[{"name":"Rajat Kumar","slug":"rajat-kumar","given":"Rajat","family":"Kumar","affiliation":""},{"name":"Kristin Predeck","slug":"kristin-predeck","given":"Kristin","family":"Predeck","affiliation":""},{"name":"Ken Meszaros","slug":"ken-meszaros","given":"Ken","family":"Meszaros","affiliation":""},{"name":"Trevor Dardik","slug":"trevor-dardik","given":"Trevor","family":"Dardik","affiliation":""}],"abstract":"Motion planning among multiple robots in a shared space is a fundamental yet computationally challenging problem in robotics, with applications ranging from warehouse automation to autonomous fleets. In this work, we introduce a fast, scalable motion planner that achieves real-time, collision-free trajectory planning via a two-staged algorithm combining deterministic search-based planning with machine learning-driven conflict resolution. We present a prioritized Safe Interval Path Planning algorithm (SIPP-PP) with a novel limited goal reservation strategy to prevent goal-blocking conflicts while allowing shared goal regions. We added a second layer of ML-guided Large Neighborhood Search (LNS) procedure to our SIPP-PP algorithm for improving success rates in highly congested environments via intelligent selection of conflict resolution actions. The result is a planning system that generates collision-free paths for multiple robots in complex environments within tens of milliseconds. For example, compared to recent advanced learning-based methods such as diffusion planners, our planner is two-to-three orders of magnitude faster. Our work demonstrates a multi-robot planner capable of real-time operation in dense scenarios, satisfying the stringent requirements of industrial applications such as drive units in fulfillment centers.","doi":"10.24963/ijcai.2026/842","url":"https://ijcai.org/proceedings/2026/842","pdf":"https://ijcai.org/proceedings/2026/0842.pdf","markdown":"https://ijcai.org/proceedings/2026/842.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/842","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Robotics","track":"AI and Robotics","number":842,"pages":{"first":7575,"last":7582},"keywords":["Robot control, planning, and execution with guarantees: Integrated task and motion planning with feedback control","Robot control, planning, and execution with guarantees: Safe and robust control under uncertainty","AIR: Robot control, planning, and execution with guarantees"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}