Ola Shorinwa
Papers
8
Total Citations
165
H-Index
6
About
Ola Shorinwa is a robotics researcher whose work sits at the intersection of distributed optimization, multi-robot coordination, and safe robot navigation. His most significant contributions lie in developing principled algorithmic frameworks that enable teams of robots to solve complex coordination problems — such as task assignment and collaborative manipulation — through local computation and peer-to-peer communication, without relying on centralized control. His 2023 paper on Consensus ADMM for distributed multirobot task assignment (47 citations) demonstrated that globally optimal solutions to combinatorial assignment problems are achievable in a fully distributed manner. Complementing this, his widely read two-part tutorial and survey series on distributed optimization for multi-robot systems (totaling 60 citations) has become a foundational reference for researchers entering the field. More recently, Shorinwa has pioneered the integration of Gaussian Splatting — a cutting-edge 3D scene representation — with robot navigation, introducing both real-time planning pipelines and control barrier function-based safety filters. With over 165 cumulative citations and contributions spanning theory, algorithms, and real-world robotics applications, Shorinwa represents an emerging leader shaping how intelligent robot teams perceive, plan, and act in complex environments.
Research Focus
Key Achievements
Top Papers
- 1Distributed Multirobot Task Assignment via Consensus ADMM47 citations · 2023
- 2
- 3Distributed Optimization Methods for Multi-Robot Systems: Part 2—A Survey29 citations · 2024
- 4Splat-Nav: Safe Real-Time Robot Navigation in Gaussian Splatting Maps22 citations · 2025
- 5Scalable Collaborative Manipulation with Distributed Trajectory Planning15 citations · 2020
- 6
- 7A Survey of Distributed Optimization Methods for Multi-Robot Systems6 citations · 2021
- 8