Martin Levihn

About

Martin Levihn is a leading researcher in robotic manipulation and autonomous navigation, with a career-defining focus on the Navigation Among Movable Obstacles (NAMO) problem. His work addresses a critical gap in robotics: enabling robots to intelligently interact with and move obstacles in unknown, cluttered environments to reach a goal. Levihn’s major contributions include pioneering hierarchical decision-theoretic planners that balance computational efficiency with optimality, as well as integrating physics-based models and reinforcement learning to handle uncertain, real-world object dynamics. His 2010 paper on NAMO in unknown environments (42 citations) and his 2014 work on physics-based priors for object-oriented MDPs (40 citations) are foundational, collectively cited over 250 times. Notably, he extended NAMO to unconventional applications, such as using environment objects as tools for door opening and assisting humanoid locomotion by manipulating the surroundings. His 2016 planner was the first to operate on a real robot with under-specified object dynamics, marking a significant step toward practical, autonomous mobile manipulation. Levihn’s research continues to shape how robots perceive, plan, and act in complex, human-centric spaces.

Research Focus

Key Achievements

Top Papers

1. 1
   Navigation Among Movable Obstacles in unknown environments

   42 citations · 2010
2. 2
   A Physics-Based Model Prior for Object-Oriented MDPs

   40 citations · 2014
3. 3
   Hierarchical Decision Theoretic Planning for Navigation Among Movable Obstacles

   39 citations · 2013
4. 4
   Locally optimal navigation among movable obstacles in unknown environments

   23 citations · 2014
5. 5
   Foresight and reconsideration in hierarchical planning and execution

   23 citations · 2013
6. 6
   Using environment objects as tools: Unconventional door opening

   19 citations · 2014
7. 7
   Planning with movable obstacles in continuous environments with uncertain dynamics

   18 citations · 2013
8. 8
   Navigation Among Movable Obstacles with learned dynamic constraints

   18 citations · 2016
9. 9
   Autonomous environment manipulation to assist humanoid locomotion

   16 citations · 2014
10. 10
    Learning non-holonomic object models for mobile manipulation

    11 citations · 2015

Key Collaborators

Contact & Links