Kiril Solovey

About

Kiril Solovey is a prominent robotics researcher whose work centers on multi-robot motion planning, algorithm design, and computational complexity. He is best known for developing the discrete-RRT (dRRT) framework, a sampling-based approach that elegantly addresses the combinatorial explosion inherent in planning coordinated paths for multiple robots simultaneously. This foundational contribution, which has accumulated over 200 citations across its key publications, introduced implicit roadmap representations that made previously intractable problems computationally feasible. A recurring theme in Solovey's research is the study of *unlabeled* multi-robot motion planning, where interchangeable robots need only reach any designated target rather than specific ones. He has rigorously examined the hardness of these problems, developed optimality-guaranteed algorithms for disc-shaped robots in cluttered environments, and explored natural generalizations such as k-color planning, where robots are grouped into interchangeable clusters. His work consistently bridges theoretical complexity analysis with practical algorithmic solutions. More recently, Solovey has extended his interests to object rearrangement with dual robotic arms and near-optimal planning with finite sampling, demonstrating a maturing research agenda with real-world application potential. His body of work has meaningfully shaped how the robotics community approaches large-scale, multi-agent coordination problems.

Research Focus

Key Achievements

Top Papers

1. 1
   Finding a Needle in an Exponential Haystack: Discrete RRT for Exploration of Implicit Roadmaps in Multi-robot Motion Planning

   118 citations · 2015
2. 2
   Finding a needle in an exponential haystack: Discrete RRT for exploration of implicit roadmaps in multi-robot motion planning

   106 citations · 2016
3. 3
   On the hardness of unlabeled multi-robot motion planning

   75 citations · 2016
4. 4
   <i>k</i> -color multi-robot motion planning

   63 citations · 2013
5. 5
   Motion Planning for Unlabeled Discs with Optimality Guarantees

   62 citations · 2015
6. 6
   Efficient Multi-Robot Motion Planning for Unlabeled Discs in Simple Polygons

   54 citations · 2015
7. 7
   On the hardness of unlabeled multi-robot motion planning

   27 citations · 2015
8. 8
   Motion Planning for Unlabeled Discs with Optimality Guarantees

   23 citations · 2015
9. 9
   Fast, High-Quality Two-Arm Rearrangement in Synchronous, Monotone Tabletop Setups

   21 citations · 2021
10. 10
    Near-Optimal Multi-Robot Motion Planning with Finite Sampling

    17 citations · 2023

Key Collaborators

Contact & Links