STIFF EU project on enhancing biomorphic agility through variable stiffness - DLR hands - logo by Ian Saunders - artificial arm and hand by TU Delft

STIFF is a research project on enhancing biomorphic agility of robot arms and hands through variable stiffness & elasticity. It is funded by the 7th framework programme of the European Union (grant agreement No: 231576).

Check our 2011 Summer School on Impedance

Institutional Partners
German Aerospace Center (DLR), Germany:
Project coordinator. Responsible for integrating a variable-impedance robotic system in the project. Development of a novel EMG system for human impedance measurements. Integration of human and robotic impedance control approaches.

Technische Universiteit Delft, Netherlands:
Responsible for modelling the human neuromuscular system from muscle to joint level. Developent of time varying system identification and parameter estimation techniques to quantify the model parameters from recorded data using haptic manipulators.

IDSIA, Switzerland:
Responsible for learning high-level task-specific controllers based on reinforcement signals for the flexible variable-impedance robot arm developed by DLR, and for inverse reinforcement learning to extract cost functions in collaboration with UEDIN.

University of Edinburgh, United Kingdom:
Responsible for the development of 'Optimal Feedback Control' based closed loop control paradigms, specifically tailored to redundant and variable impedance actuators. Developing methods to extract cost functions and comparing control policies to evaluate improvement in performance when modulating impedance optimally.

Université Paris Descartes - CNRS, France:
Responsible for studies of impedance control in humans, using a variety of techniques including direct physiologicial measurements (EMG, H-reflex), mathematical modeling and robotic simulation. The main emphasis is 1) to suggest biologically-inspired strategies to be applied to robotics control and 2) to use analogies with robotic devices to better understand human behaviour in terms of impedance.

artificial DLR hand grabs a glass; humanoid robot javelin thrower cartoon by Juergen Schmidhuber




Human Hand-Arm Modelling

The role of TUD is to provide physiologically realistic human models of the neuromechanical system and to develop identification algorithms for estimation of human joint impedance.

Continuous estimation of time-varying joint impedance provides important information for the analysis of human movement control. At TUD we developed a new identification algorithm that enables high temporal resolution impedance estimation during e.g. goal directed arm movements. The next step will be to derive feedforward and feedback control properties from the estimated endpoint impedances for various movement tasks and loading conditions.

The Delft Shoulder and Elbow Model is extended with a wrist joint (2 DoF saddle joint) based on human morphological data. A hand model was developed incorporating 21 DoF (4 for each finger and the thumb, one for the hand palm). All relevant muscle attachments were measured on the same cadaver as has been used to derive the anatomical data for the shoulder and arm.  The wrist-hand model is built in SPACAR, the multimode computer package that has also been used for the arm and shoulder. Ultrasound was used to measure tendon excursions for individual subjects in vivo in order to derive individual moment arms and interaction between the fingers.

A state space version of the Huxley muscle model has been developed and parameterized on in vivo human data. Nonlinear mechanical features of muscles, such as the short range stiffness, was well captured by the model. Further, a user interface has been developed for analysis of muscle spindle functioning (length/velocity sensors of muscles). Both the muscle and spindle model are ready for implementation in the DSEM.

artificial DLR arm and hand; artificial hand squeezes STIFF
artificial DLR hand holding a wine bottle