IEEE Robotics & Automation Magazine - March 2016 - 27

joints successfully reproducing human balancing responses
in model simulations and in a special purpose 2-DoF robot
called Posturob II [22].
In this article, the DEC controller was transferred to the
control of TORO, which meant entering unknown ground.
The controller was extended to include the knee joints by
adding one more module to the ankle and hip modules [Figure 4(b)]. In humans, knee joint contributions tend to be
minor with sagittal plane disturbances [23], [24]. Qualitatively, the knees tend to be locked in an extended position during moderate disturbances with the system in steady state,
while strong transient disturbances tend to evoke a transient
knee bending. Not knowing yet exactly the human knee responses during transient disturbances, the knee control
module was set to the task of joint angle stabilization with a
bent position of 39° from the straight position. Missing DEC
model settings for transient responses, we resorted to the
DEC model established for steady-state balancing. The ankle
module was set to maintain a whole body COM lean of 0.9°
forward, and the hip module was set to maintain an upper
body orientation of 0.9° forward.
The implementation of the estimators of contact forces
and the support surface translation into TORO did not bring
signiﬁcant improvements, and it increased the danger of control instability in some cases. Therefore, only the tilt and gravity estimators were active in the current study, having
previously observed that these estimators make the major
contribution to balance control [22].
In each control module, the gravitational torque is estimated and compensated in the general form
xt grav, i

= w grav , i m i gh i sin (a BS, i),

(5)

where the index i addresses a joint (i.e., hip joint is one, knee
joint is two, and ankle joint is three), a BS, i represents the space
orientation of the COM of all segments above the ith joint,
w grav, i is a weighting factor, and m i and h i represent the total
mass and COM height of all segments above the ith joint, respectively. For small angles, we assume that h i is constant and
sin (a BS, i) = a BS, i . To obtain a BS, i, the tilt disturbance, i.e., the
space orientation of the supporting link (a space, j), is required.
This is estimated from vestibular (a vest) and proprioceptive
(a prop, i) signals in the form
at space, j

j-1

= a vest - / a prop, i .
i=1

(6)

The combination of vestibular and proprioceptive signals
allows controlling the system in space coordinates. Previous
studies suggested the presence of thresholds and weighting
factors in the estimators and a servo loop gain to reproduce
human data. Since the aim of this article was not to reproduce
human results but to compare the two balance controllers, the
servo loop gain w s was set to 0.8. The thresholds were not applied, and the weighting factors were set close to unity
(w c = 0.95) to achieve a disturbance compensation close to
the ideal one. The total control torque x i applied at each joint
(ankle, knee, and hip) at time t consists of an instantaneous

(passive) joint-level PD action with setpoint q d, i and a delay
affected servo loop, including a PD action on a BS, i:
x i (t)

= x p, i (t) + x a, i (t - Dt)
x p, i = - K p, i (q i - q d, i) - D p, i qo i
x a, i = - w c xt est, i - xt grav, i + x PD, i - x p, i
x PD, i = w s (K a, i (a d, i - a BS, i) + D a, i (ao d, i -

ao BS, i)) .

(7)

The control torque x a, i, representing the SL and LL feedback loops, is affected by a time delay Dt. The effects of the
inner passive control action x p, i as well as other
estimated disturbances
The combination
xt est, i are taken into account in x a, i . The values
of vestibular and
of the passive controller
gains, K p, i and D p, i, were
proprioceptive signals
set to 15% of the active
controller gains, K a, i and
allows controlling
D a, i, according to values
observed in humans [15].
the system in space
For technical reasons, the
a vest signal from the IMU
coordinates.
and the input into the derivative part of the controllers were processed with a ﬁrst-order low pass-ﬁlter with
the cutoff frequencies of 30 and 5 Hz, respectively.
Hypotheses and Concept of Experiments
Experimental Hypotheses
The two control approaches presented in the previous sections
aim at balancing against external perturbations. They have
been derived from different perspectives. The model based
balancer (MC controller) uses a complete dynamical model of
the humanoid robot and aims at controlling the force distribution while achieving a compliant behavior. On the other hand,
the DEC controller was derived from observations of the
human balancing behavior. It employs a modular control
concept for the ankle, knee, and hip joints. Moreover, it applies
a fast joint-level feedback loop representing the human muscle
properties in addition to the DEC. This fast joint-level control
action plays an important role in human balancing due to the
different time delays of the neural feedback loops.
This motivates the hypothesis that the passive loop of the
DEC controller leads to increased robustness against time
delay and slow controller sampling. However, it is expected
that the modular control approach of the DEC controller
leads to stronger dynamic couplings in the transient response
than in the case of the model-based controller that uses a centralized control structure.
Implementation
We conducted several experiments for both controllers using
the TORO [8], [25]. In the current version, TORO has 27 DoF
(excluding the hands), a height of 1.74 m, and a weight of 76 kg.
march 2016

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Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - March 2016