activity
20192026
most citedLearning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates

48 citations · 83 across the 10 of their papers we have counts for

collaborators

10 papers

cs.RO2026

Many-to-Many Multi-Agent Pickup and Delivery

Ethan Schneider, Jingkai Chen, Tianyi Gu +3

Multi-robot systems in automated warehouses must manage continuous streams of pickup-and-delivery tasks while ensuring efficiency and safety. Prior work on Multi-Agent Pickup-and-D…

cs.AI2026

Learning-guided Prioritized Planning for Lifelong Multi-Agent Path Finding in Warehouse Automation

Han Zheng, Yining Ma, Brandon Araki +2

Lifelong Multi-Agent Path Finding (MAPF) is critical for modern warehouse automation, which requires multiple robots to continuously navigate conflict-free paths to optimize the ov…

cs.RO2022★ 9 cited

Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding

Ruipeng Zhang, Chenning Yu, Jingkai Chen +2

Learning-based methods have shown promising performance for accelerating motion planning, but mostly in the setting of static environments. For the more challenging problem of plan…

cs.RO2022★ 20 cited

Cooperative Task and Motion Planning for Multi-Arm Assembly Systems

Jingkai Chen, Jiaoyang Li, Yijiang Huang +7

Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs.…

cs.RO2022★ 2 cited

Multi-agent Motion Planning from Signal Temporal Logic Specifications

Dawei Sun, Jingkai Chen, Sayan Mitra +1

We tackle the challenging problem of multi-agent cooperative motion planning for complex tasks described using signal temporal logic (STL), where robots can have nonlinear and nonh…

cs.AI2021

Generalized Conflict-directed Search for Optimal Ordering Problems

Jingkai Chen, Yuening Zhang, Cheng Fang +1

Solving planning and scheduling problems for multiple tasks with highly coupled state and temporal constraints is notoriously challenging. An appealing approach to effectively deco…