Human Machine Interaction and Communication in Cooperative Actions - Bioinspired Approaches for Human-Centric Technologies - page 252

equilibrium and the stiffness via proper agonist/antagonist muscles co-contraction. It
is worth stressing here that even if deafferentation (i.e., total removal of feedback) is
just an extreme situation, we just discussed that there might be tasks (Burdet
et al. 2001 ) where relying on feedback would prevent the task achievement in
presence of delays and unpredictability.
The importance of the agonist/antagonist muscle arrangement in dealing with
the minimization of uncertainties has been recently studied in Mitrovic
et al. ( 2010a ). In particular, it was shown that the tool of stochastic optimal control
can efficiently simulate the impedance regulation principles observed in humans
performing stationary and adaptive tasks. Similar to the work presented in Mitrovic
et al. ( 2010a ), we will make extensive use of a state-of-the-art optimization tool
(ILQG, Todorov and Li 2005 ) to solve the problem of planning movements in
presence of uncertainties but extending results to two-degrees-of-freedom models
of the human arm. Instead of focusing only on realistic models of human muscles,
we will consider also other actuators whose dynamical model covers a number
passive variable stiffness actuators recently designed for robotic applications (Wolf
and Hirzinger 2008 ; Jafari et al. 2010 ). Moreover, differently from the approach
proposed in Mitrovic et al. ( 2010b ), we focus on purely feedforward control thus
neglecting the possibility of using feedback to correct online the motor plan.
Driven by the considerations above, roboticists in the last decade have started
studying mechanical solutions capable of controlling the system structural stiffness
by proposing a number of solutions, which fall under the broad category of passive
variable stiffness actuators (pVSA). All these systems use different principles (cam
mechanisms, nonlinear springs, etc.) to change the rigidity between actuator and
joint in order to mimic human ability to change the body compliance by regulating
the muscle co-activation. Although the proposed designs possess interesting fea-
tures, it has been recently pointed out that available solutions strongly rely on
feedback control strategies and differ (as to this concern) from human muscles
(Berret et al. 2011 ). Specifically, simulations conducted by Berret et al. ( 2011 )
indicate that muscle models outperform available pVSA solutions in dealing with
unpredictable (i.e., stochastic) perturbations. These results motivated the design of
a different type of pVSA (Berret et al. 2012 ; Nori et al. 2012 , Fig. 8.4 ) possessing a
novel property that we named passive noise rejection variable stiffness actuators
(pnrVSA). The design was inspired by recent motor control experiments showing
that humans adopt muscle co-activation as a strategy to deal with highly unstable
force fields in the presence of significant proprioceptive delays. Preliminary results
(Berret et al. 2013 ) show that the proposed solution outperforms conventional
solutions in handling interaction tasks in presence of uncertainties.
8.3.3 Stiffness vs. Feedback Strategy in Unstable
Interactions
Although modulation of the musculoskeletal stiffness via muscle co-contraction
can compensate stochastic and unpredictable disturbances, as shown in the already