Mark Moll

About

Mark Moll is a prominent robotics researcher whose work spans motion planning, modular robotics, and computational biology. He is perhaps best known as a principal developer of the Open Motion Planning Library (OMPL), a landmark contribution to the robotics community that has become a foundational tool for sampling-based motion planning research, amassing over 1,600 citations since its 2012 release. OMPL's accessible, extensible design has enabled countless researchers and engineers to implement and benchmark state-of-the-art planning algorithms with minimal overhead. Moll has also made significant contributions to modular self-reconfigurable robotics, co-authoring a widely cited grand challenges overview (1,020 citations) and contributing to the SUPERBOT system, advancing the vision of robots that autonomously adapt their morphology for tasks ranging from search-and-rescue to space exploration. His research extends into planning for deformable objects, constrained motion planning for high-degree-of-freedom systems, and integrated task and motion planning, reflecting a remarkably broad research portfolio. Notably, Moll has also applied motion planning techniques to computational biology, developing algorithms to trace conformational changes in proteins. His benchmarking infrastructure for motion planning algorithms further underscores his commitment to rigorous, reproducible robotics science, making his work invaluable to both newcomers and seasoned researchers in the field.

Research Focus

Key Achievements

Top Papers

1. 1
   The Open Motion Planning Library

   1,600 citations · 2012
2. 2
   Modular Self-Reconfigurable Robot Systems [Grand Challenges of Robotics]

   1,020 citations · 2007
3. 3
   SUPERBOT: A Deployable, Multi-Functional, and Modular Self-Reconfigurable Robotic System

   304 citations · 2006
4. 4
   Path planning for deformable linear objects

   204 citations · 2006
5. 5
   Sampling-Based Methods for Motion Planning with Constraints

   185 citations · 2018
6. 6
   Benchmarking Motion Planning Algorithms: An Extensible Infrastructure for Analysis and Visualization

   123 citations · 2015
7. 7
   Randomized Physics-Based Motion Planning for Grasping in Cluttered and Uncertain Environments

   94 citations · 2017
8. 8
   Tracing conformational changes in proteins

   83 citations · 2010
9. 9
   Experience-based planning with sparse roadmap spanners

   73 citations · 2015
10. 10
    SMT-based synthesis of integrated task and motion plans from plan outlines

    62 citations · 2014

Key Collaborators

Contact & Links