Matthew Howard

About

Matthew Howard is a prominent robotics researcher whose work spans robot learning, variable impedance actuation, and control of complex robotic systems. His research has made substantial contributions to understanding how robots can perform dynamic, high-performance tasks by exploiting physical compliance. His highly cited work on variable stiffness control (120 citations) and explosive movement tasks (64 citations) established foundational frameworks for how robots with variable impedance actuators can optimally coordinate motion and stiffness to achieve powerful, efficient movements — a challenge previously considered prohibitively complex. Howard has also advanced the field of continuum robotics, co-developing TMTDyn (86 citations), a widely adopted MATLAB package providing accessible dynamic modeling tools for hybrid rigid-continuum robots. His research into robot learning from demonstration has been equally influential, with contributions addressing how human impedance behavior can be transferred to robotic systems (57 citations) and how to quantify and improve human teaching in robot training scenarios (47 citations). His work on dimensionality reduction for reinforcement learning (42 citations) offers practical strategies for overcoming the notorious curse of dimensionality in robotic control. Collectively, Howard's research bridges theoretical rigor and real-world applicability, making him a significant figure in modern robotics and human-robot interaction research.

Research Focus

Key Achievements

Top Papers

1. 1
   Optimal variable stiffness control: formulation and application to explosive movement tasks

   120 citations · 2012
2. 2
   <i>TMTDyn</i> : A Matlab package for modeling and control of hybrid rigid–continuum robots based on discretized lumped systems and reduced-order models

   86 citations · 2020
3. 3
   Exploiting Variable Stiffness in Explosive Movement Tasks

   64 citations · 2011
4. 4
   Transferring Human Impedance Behavior to Heterogeneous Variable Impedance Actuators

   57 citations · 2013
5. 5
   2025 IEEE International Conference on Robotics and Automation (ICRA)

   49 citations · 2025
6. 6
   Quantifying teaching behavior in robot learning from demonstration

   47 citations · 2019
7. 7
   Exploiting variable physical damping in rapid movement tasks

   46 citations · 2012
8. 8
   Using dimensionality reduction to exploit constraints in reinforcement learning

   42 citations · 2010
9. 9
   Learning null space projections

   33 citations · 2015
10. 10
    A novel method for learning policies from variable constraint data

    32 citations · 2009

Key Collaborators

Contact & Links