Transferring Impedance Control Strategies Between Heterogeneous Systems via Apprenticeship Learning

Matthew Howard, Djordje Mitrovic, Sethu Vijayakumar

Research output: Chapter in Book/Report/Conference proceedingConference paper

12 Citations (Scopus)


We present a novel method for designing controllers for robots with variable impedance actuators. We take an imitation learning approach, whereby we learn impedance modulation strategies from observations of behaviour (for example, that of humans) and transfer these to a robotic plant with very different actuators and dynamics. In contrast to previous approaches where impedance characteristics are directly imitated, our method uses task performance as the metric of imitation, ensuring that the learnt controllers are directly optimised for the hardware of the imitator. As a key ingredient, we use apprenticeship learning to model the optimisation criteria underlying observed behaviour, in order to frame a correspondent optimal control problem for the imitator. We then apply local optimal feedback control techniques to find an appropriate impedance modulation strategy under the imitator's dynamics. We test our approach on systems of varying complexity, including a novel, antagonistic series elastic actuator and a biologically realistic two-joint, six-muscle model of the human arm.
Original languageEnglish
Title of host publication2010 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids)
Number of pages8
ISBN (Print)978-1-4244-8688-5
Publication statusPublished - 2010


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