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


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

STIFF

How can stiffness & elasticity enhance performance of human and robotic arms? Our studies combine biophysical models and machine learning to optimally control a human-like robotic system.
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Many industrial robots are much stronger than humans, but also very inflexible. For example, humans can throw objects much further and catch them much more gracefully, temporarily storing energy in elastic tendons and muscles. Such flexible actuators, however, require more sophisticated control algorithms than those used by traditional robots.

The goal of the STIFF consortium is to equip a highly biomimetic robot hand-arm system with the agility, robustness and versatility that are the hallmarks of the human motor system, by understanding and mimicking the variable stiffness paradigms that are so effectively employed by the human central nervous system. A key component of our study will be the anatomically accurate musculoskeletal modelling of the human arm and hand.

The project will develop novel methodologies to comprehend how the human arm can adapt its impedance, e.g., by changing the co-contraction level or by adapting reflex gains. The impedances of arm and hand will be investigated using powerful robot manipulators capable of imposing force perturbations. While stiffness & elasticity are currently exploited in the context of artificial laboratory tasks, we will investigate stiffness-dependent behaviour in natural tasks such as throwing a ball or inserting a peg in a hole.

Existing closed-loop system identification techniques will be extended by non-linear time-variant techniques to identify the behaviour during reaching and grasping tasks. Grasp force modulation and hand muscle activity correlations will be acquired through machine learning techniques and then transferred to the robotic system. Finally, optimization techniques gleaned and validated on the detailed biophysical model will be transferred to the variable impedance actuation of the novel biomorphic robot.

 artificial DLR arm and hand; artificial hand squeezes STIFF

Latest publications from STIFF:

  1. Smagt, P. van der and Helm, F. van der and Schmidhuber, J. and Vijayakumar, S. and McIntyre , J. (2010). Enhancing biomorphic agility through variable stiffness. Proc. 4th International Conference on Cognitive Systems Zürich [BibTex]

  2. Koutnik, J. and Gomez, F. and Schmidhuber, J. (2010). Searching for Minimal Neural Networks in Fourier Space. Proceedings of The Third Conference on Artificial General Intelligence (AGI 2010) [pdf] [BibTex]

  3. Mitrovic, D. and Nagashima, S. and Klanke, S. and Matsubara, T. and Vijayakumar, S. (2010). Optimal Feedback Control for Anthropomorphic Manipulator. Proc. IEEE International Conference on Robotics and Automation [BibTex]

  4. Mitrovic, D. and Klanke, S. and Vijayakumar, S. (2010). Adaptive Optimal Feedback Control with Learned Internal Dynamics Models. In Olivier Sigaud and Jan Peters (Eds.) From Motor Learning to Interaction Learning in Robots Springer Berlin / Heidelberg: 65-84. [pdf] [doi] [BibTex]

  5. Howard, M. and Klanke, S. and Gienger, M. and Goerick, C. and Vijayakumar, S. (2010). Methods for Learning Control Policies from Variable-constraint Demonstrations. In Olivier Sigaud and Jan Peters (Eds.) From Motor Learning to Interaction Learning in Robots Springer Berlin / Heidelberg: 253-291. [pdf] [doi] [BibTex]

  6. Vijayakumar, S. and Toussaint, M. and Petkos, G. and Howard, M. (2009). Planning and Moving in Dynamic Environments: A statistical machine learning approach. In Sendhoff, Koerner, Sporns, Ritter, Doya (Eds.) Creating Brain-Like Intelligence Springer-Verlag: 151-191. [doi] [www] [BibTex]

full publication list
artificial DLR hand holding a wine bottle