Kim D. Listmann

About

Kim D. Listmann is a leading researcher in robotics, specializing in force control, multi-robot coordination, and skill learning for autonomous systems. Their most impactful work, a 2017 paper on motor-current-based estimation of Cartesian contact forces and torques (181 citations), introduced a Kalman filter approach that eliminates the need for additional sensors, enabling robust force control in robotic manipulators. This innovation is complemented by their development of MPC-based admittance control (42 citations) and iterative learning methods for assembly tasks in unstructured environments (17 citations). In multi-robot systems, Listmann pioneered the "DisCoverage" paradigm (48 citations), a frontier-based exploration strategy that merges coverage and coordination for efficient multi-robot exploration, with extensions to non-convex environments (12 citations). Their earlier work on consensus-based formation control for nonholonomic mobile robots (90 citations) established foundational stability proofs using artificial potential fields. More recently, Listmann has advanced skill learning systems (5 citations) and deep Lagrangian networks for energy-based control (5 citations), bridging deep learning with classical control theory. With over 400 total citations, their contributions have significantly impacted both theoretical foundations and practical applications in robotics, particularly in force-sensitive manipulation and distributed autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Motor-Current-Based Estimation of Cartesian Contact Forces and Torques for Robotic Manipulators and Its Application to Force Control

   181 citations · 2017
2. 2
   Consensus for formation control of nonholonomic mobile robots

   90 citations · 2009
3. 3
   DisCoverage: A new paradigm for multi-robot exploration

   48 citations · 2010
4. 4
   MPC-based admittance control for robotic manipulators

   42 citations · 2016
5. 5
   Iteratively Learned and Temporally Scaled Force Control with application to robotic assembly in unstructured environments

   17 citations · 2017
6. 6
   DisCoverage: From Coverage to Distributed Multi-Robot Exploration

   14 citations · 2013
7. 7
   DisCoverage for non-convex environments with arbitrary obstacles

   12 citations · 2011
8. 8
   Building Skill Learning Systems for Robotics

   5 citations · 2021
9. 9
   Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems

   5 citations · 2019
10. 10
    MM-ulator: Towards a Common Evaluation Platform for Mixed Mode Environments

    5 citations · 2008

Key Collaborators

Contact & Links