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




VIA Robotics and Control

Variable impedance actuation has become increasingly popular in the design and control of novel robotic mechanisms. Variable impedance actuators (VIAs) promise many benefits for the next generation of robots, including (i) increased safety in settings where there is human-robot interaction, (ii) increased dynamic range and (iii) increased energy efficiency when interacting with the environment. However, despite these benefits, there are still a number of challenges associated with deploying such actuators to the current generation of robots. One major problem is that of how to control such mechanisms, and in particular, how to best use variable impedance so that the benefits (such as compliance) are realised, while compromise on other aspects of performance (such as precision) is avoided. In this part of the project, we address the control issues encountered for robots with variable stiffness. In particular, we focus on two aspects of the problem (i) optimal control methods suitable for exploiting the natural dynamics of a variable stiffness system, and (ii) methods for transferring impedance modulation strategies from behavioural measurements of humans to robotic systems.

UEDIN have developed simple robotic variable-impedance systems on which we demonstrated optimal methods for exploiting variable impedance actuation,  in order to verify the benefits of variable stiffness. In addition, UEDIN has developed a new approach to optimal control directed at resolving timing issues, including optimal selection of movement durations and timing of intermediate task goals. In the meantime, DLR has developed a hyperrealistic variable-impedance robotic hand-arm system "HASy". This system integrates 52 dc motors for antagonistic arm and hand movement, allowing separate setting of the mechanical stiffness of each of its joints.

To transfer the human stiffness behaviour to this robotic system,  IDSIA investigates inverse reinforcement learning (IRL), or cost function identification. The first deals with global search problems, e.g. deciding which control trajectories avoid obstacles and bad local optima (such as getting stuck behind an obstacle). To solve them we use direct search in a space of indirect encodings of solution candidates, such as recurrent neural networks. In the second topic, IDSIA has developed an evolutionary approach to IRL, and demonstrated its feasibility on a number of simulated benchmark tasks. The framework was applied to experimental data from various human movement studies, where it extracted optimality principles (and thus building blocks of control laws)  from trajectory data obtained under laboratory conditions. This key step should allow us to transfer control principles of humans to the biomimetic DLR robot system.

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