Data-Driven Disturbance Observers for Estimating External Forces on Soft Robots

Cosimo Della Santina*, Ryan Landon Truby, Daniela Rus

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


Unlike traditional robots, soft robots can intrinsically interact with their environment in a continuous, robust, and safe manner. These abilities - and the new opportunities they open - motivate the development of algorithms that provide reliable information on the nature of environmental interactions and, thereby, enable soft robots to reason on and properly react to external contact events. However, directly extracting such information with integrated sensors remains an arduous task that is further complicated by also needing to sense the soft robot's configuration. As an alternative to direct sensing, this paper addresses the challenge of estimating contact forces directly from the robot's posture. We propose a new technique that merges a nominal disturbance observer, a model-based component, with corrections learned from data. The result is an algorithm that is accurate yet sample efficient, and one that can reliably estimate external contact events with the environment. We prove the convergence of our proposed method analytically, and we demonstrate its performance with simulations and physical experiments.

Original languageEnglish (US)
Article number9145617
Pages (from-to)5717-5724
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number4
StatePublished - Oct 2020
Externally publishedYes


  • Modeling
  • and learning for soft robots
  • contact modeling
  • control
  • model learning for control

ASJC Scopus subject areas

  • 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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