Papers
10
Total Citations
173
H-Index
6
About
James Motes is a robotics researcher specializing in multi-robot systems, with a particular focus on task and motion planning, multi-robot motion planning, and conflict resolution in complex environments. His work addresses some of the most challenging scalability problems in robotics, developing algorithms that enable teams of robots to coordinate efficiently across cluttered, real-world settings. Motes is perhaps best known for TMP-CBS, introduced in his highly cited 2020 paper (54 citations), which extended Conflict-Based Search from pathfinding into integrated task and motion planning to handle sequential subtask dependencies. His complementary work on representation-optimal multi-robot motion planning (53 citations) bridged the gap between discrete multi-agent pathfinding and continuous state-space planning. His 2023 hypergraph-based approach dramatically accelerated object rearrangement planning — achieving solutions up to three orders of magnitude faster than prior methods — while his topological guidance work tackles the notoriously difficult problem of navigating congested environments like warehouse aisles. More recently, Motes introduced the Adaptive Robot Coordination (ARC) framework, a flexible hybrid approach to resolving inter-robot conflicts, extended further to kinodynamic planning in 2025. With over 170 cumulative citations, his research represents a sustained and impactful contribution to making multi-robot systems practical, scalable, and deployable in real-world applications.
Research Focus
Key Achievements
Top Papers
- 1Multi-Robot Task and Motion Planning With Subtask Dependencies54 citations · 2020
- 2
- 3Hypergraph-Based Multi-robot Task and Motion Planning23 citations · 2023
- 4
- 5Interaction Templates for Multi-Robot Systems11 citations · 2019
- 6
- 7
- 8Roadmap-Optimal Multi-robot Motion Planning using Conflict-Based Search.3 citations · 2019
- 9Evaluating Guiding Spaces for Motion Planning2 citations · 2022
- 10K-ARC: Adaptive Robot Coordination for Multi-Robot Kinodynamic Planning1 citations · 2025