Lowell Rose

About

Lowell Rose is a leading researcher in the field of rehabilitation robotics, with a primary focus on the development and application of powered lower-body exoskeletons for post-stroke gait rehabilitation. His work uniquely bridges the gap between advanced control theory and real-world clinical needs. Rose’s major contributions include pioneering the use of model-free deep reinforcement learning to create adaptive exoskeleton control systems that provide "assistance-as-needed," thereby maximizing user participation and motor learning during therapy. He has also developed comprehensive frameworks for mapping and controlling gait patterns across both simulation and real-world environments, facilitating on-the-fly customization. His highly cited 2020 qualitative study (85 citations) on the perspectives of stroke survivors and physiotherapists provides critical user-centered insights for device adoption. With a career spanning from early work on the architecture of the ROBODOC surgical robot to his current focus on intelligent exoskeletons, Rose is notable for translating complex computational methods into practical, human-centric rehabilitation tools.

Research Focus

Key Achievements

Top Papers

1. 1
   Exoskeleton use in post-stroke gait rehabilitation: a qualitative study of the perspectives of persons post-stroke and physiotherapists

   85 citations · 2020
2. 2
   A model-free deep reinforcement learning approach for control of exoskeleton gait patterns

   41 citations · 2021
3. 3
   End-to-End Deep Reinforcement Learning for Exoskeleton Control

   27 citations · 2020
4. 4
   Architecture of a surgical robot

   25 citations · 2003
5. 5
   A Framework for Mapping and Controlling Exoskeleton Gait Patterns in Both Simulation and Real-World

   7 citations · 2020

Key Collaborators

Contact & Links