- Abstract:
-
Novel anthropomorphic robotic systems increasingly employ variable impedance actuation in order to achieve robustness to uncertainty, superior agility and efficiency that are hallmarks of biological systems. Controlling and modulating impedance profiles such that it is optimally tuned to the controlled plant is crucial to realise these benefits. In this work, we propose a methodology to generate optimal control commands for variable impedance actuators under a prescribed trade-off of task accuracy and energy cost. In contrast to classical optimal control methods that typically require an accurate analytical plant dynamics model, we employ a supervised learning paradigm to acquire both the process dynamics as well as the stochastic properties. This enables us to prescribe an optimal impedance and command profile (i) tuned to the hard-to-model stochastic characteristics of a plant and (ii) adapt to the systematic changes such as friction or loading. To evaluate the scalability of our framework to real hardware, we designed and built a novel antagonistic series elastic actuator (SEA) characterised by a simple mechanical architecture and we ran rigorous evaluations on a variety of reach and hold tasks. These results highlight, for the first time on real hardware, how impedance modulation profiles tuned to the plant dynamics emerge from the first principles of optimization. Furthermore, we illustrate how changes in plant dynamics and stochastic characteristics (e.g., while using a power tool) can be accounted for using this adaptation paradigm, achieving clear performance gains over classical methods that ignore or are incapable of incorporating this information.
- Copyright:
- 2010 by The University of Edinburgh. All Rights Reserved
- Links To Paper
- No links available
- Bibtex format
- @Misc{EDI-INF-RR-1359,
- author = {
Djordje Mitrovic
and Klanke Klanke
and
},
- title = {Exploiting Sensorimotor Stochasticity for Learning Control of Variable Impedance Actuators},
- year = 2010,
- month = {Jan},
- note = {Technical Report},
- }
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