Co-exploring Actuator Antagonism and Bio-inspired Control in a Printable Robot Arm

  • Martin F. Stoelen
  • Fabio Bonsignorio
  • Angelo Cangelosi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9825)


The human arm is capable of performing fast targeted movements with high precision, say in pointing with a mouse cursor, but is inherently ‘soft’ due to the muscles, tendons and other tissues of which it is composed. Robot arms are also becoming softer, to enable robustness when operating in real-world environments, and to make them safer to use around people. But softness comes at a price, typically an increase in the complexity of the control required for a given task speed/accuracy requirement. Here we explore how fast and precise joint movements can be simply and effectively performed in a soft robot arm, by taking inspiration from the human arm. First, viscoelastic actuator-tendon systems in an agonist-antagonist setup provide joints with inherent damping, and stiffness that can be varied in real-time through co-contraction. Second, a light-weight and learnable inverse model for each joint enables a fast ballistic phase that drives the arm close to a desired equilibrium point and co-contraction tuple, while the final adjustment is done by a feedback controller. The approach is embodied in the GummiArm, a robot which can almost entirely be printed on hobby-grade 3D printers. This enables rapid and iterative co-exploration of ‘brain’ and ‘body’, and provides a great platform for developing adaptive and bio-inspired behaviours.


Bio-inspiration Learnable models Agonist-antagonist joints Variable stiffness 3D printing Targeted movements 



This work was funded by a Marie Curie Intra-European Fellowship within the 7th European Community Framework Programme (DeCoRo FP7-PEOPLE-2013-IEF).


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Martin F. Stoelen
    • 1
  • Fabio Bonsignorio
    • 2
  • Angelo Cangelosi
    • 1
  1. 1.Centre for Robotics and Neural SystemsPlymouth UniversityPlymouthUK
  2. 2.The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa and Heron RobotsGenovaItaly

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