Force/Torque-Sensorless Joint Stiffness Estimation in Articulated Soft Robots

Maja Trumic, Adriano Fagiolini, Giorgio Grioli, Kosta Jovanovic, Maja Trumic

Research output: Contribution to journalArticlepeer-review


Currently, the access to the knowledge of stiffness values is typically constrained to a-priori identified models or datasheet information, which either do not usually take into ac- count the full range of possible stiffness values or need extensive experiments. This work tackles the challenge of stiffness estimation in articulated soft manipulators, and it proposes an innovative solution adding value to the previous research by removing the necessity for force/torque sensors and generalizing to multi-degree- of-freedom robots. Built upon the theory of unknown input-state observers and recursive least-square algorithms, the solution is independent of the actuator model parameters and its internal control signals. The validity of the approach is proven analytically for single and multiple degree-of-freedom robots. The obtained estimators are first evaluated via simulations on articulated soft robots with different actuations and then tested in experiments with real robotic setups using antagonistic variable stiffness actuators.
Original languageEnglish
Pages (from-to)7036-7043
Number of pages8
JournalIEEE Robotics and Automation Letters
Publication statusPublished - 2022

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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