Thomas George Thuruthel

About

Thomas George Thuruthel is a pioneering researcher at the intersection of soft robotics, machine learning, and intelligent sensing systems. His work has fundamentally advanced how soft robots perceive, model, and control themselves in complex environments. Thuruthel's most influential contributions include developing machine learning frameworks for soft robot perception using embedded sensors and recurrent neural networks (648 citations), and exploring electronic skins combined with AI to enable tactile sensing and proprioception in autonomous soft robots (680 citations). His comprehensive survey on control strategies for soft robotic manipulators (624 citations) has become an essential reference for the field, while his model-based reinforcement learning approaches for dynamic control (390 citations) have pushed the boundaries of what soft robotic systems can achieve in real-time applications. Beyond control and perception, Thuruthel has made notable contributions to soft materials science, reviewing self-healing polymers for robotics (295 citations) and developing innovative ionic hydrogel strain sensors (140 citations). His research spans the full stack of soft robotics — from materials to control architectures — with a cumulative citation impact exceeding 3,000, cementing his reputation as a transformative voice in next-generation robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Electronic skins and machine learning for intelligent soft robots

   680 citations · 2020
2. 2
   Soft robot perception using embedded soft sensors and recurrent neural networks

   648 citations · 2019
3. 3
   Control Strategies for Soft Robotic Manipulators: A Survey

   624 citations · 2018
4. 4
   Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators

   390 citations · 2018
5. 5
   A review on self-healing polymers for soft robotics

   295 citations · 2021
6. 6
   Learning dynamic models for open loop predictive control of soft robotic manipulators

   144 citations · 2017
7. 7
   Self-healing ionic gelatin/glycerol hydrogels for strain sensing applications

   140 citations · 2022
8. 8
   Learning Closed Loop Kinematic Controllers for Continuum Manipulators in Unstructured Environments

   136 citations · 2017
9. 9
   Stable Open Loop Control of Soft Robotic Manipulators

   92 citations · 2018
10. 10
    Improving Robotic Cooking Using Batch Bayesian Optimization

    65 citations · 2020

Key Collaborators

Contact & Links