Human Motion Reproduction by Torque-based Humanoid TrackingControl for Active Assistive Device Evaluation

Takahiro Ito1 2, Ko Ayusawa2, Eiichi Yoshida1 2, and Hiroshi Kobayashi3

Abstract— In a super-aged society such as Japan, wearableassistive devices that aim at reducing caregiver burden as wellas improving the autonomy of the elderly are attracting stronginterests. Humanoid robots can be used to evaluate the sup-portive effects of assistive devices by measuring joint torques,as that cannot be directly obtained from human subjects. Whileour previous work proposed a humanoid-based method forestimating static supportive torques of powerful and activesupportive devices, this paper proposes a novel frameworkfor evaluating their supportive effects during dynamic motion.Assuming that humans move with minimum exertion whentaking full advantage of a device’s assistive power, we proposeusing a controller on a humanoid to track retargeted humanmotions during power assistance. The effectiveness of the pro-posed method has been validated by experimentally assessing anassistive device (Muscle Suit) actuated by pneumatic artificialmuscles.

I. INTRODUCTION

With a view to provide solutions for super-aged societies,robotics technology has attracted attention in Japan as a keytechnology for reducing caregiver workload and increasingthe autonomy of elderly people. Commercially availableproducts [1], [2], [3], include support devices for walking[4], for monitoring one’s health [5], for heavy tasks [6], [7],and for reducing caregiver load [8].

Wearable devices (also called “exoskeletons”) are apromising solution for caregivers or for anyone who performstasks involving heavy loads. These lightweight devices aredesigned to be worn easily and function to reduce load tothe lower back. For these devices to be widely diffused, theymust be quantitatively evaluated to enable users to compareproducts and to select one that is suitable for their purpose.A common method of evaluation is to have human sub-jects test the product and answer a questionnaire. Althoughthis method has the advantage of direct human input, anyattempts to quantify the evaluation is difficult because ofthe inherently subjective nature of questionnaires. Moreover,human experiments risk injury, may be non-repeatable, andare encumbered by ethical restrictions.

Some studies have already proposed using a humanoidrobot to evaluate assistive devices in order to avoid using hu-man subjects [9], [10]. Miura et al. reported on the supportiveeffect of a passive assistive suit, which was evaluated using

*This research was partly supported by METI/AMED Robotic Devicesfor Nursing Care Project.

T. Ito and E. Yoshida are with 1University of Tsukuba, Japan.T. Ito, K. Ayusawa and E. Yoshida are with 2CNRS-AIST JRL (JointRobotics Laboratory), UMI3218/RL, Tsukuba, Japan. H. Kobayashi is with3Tokyo University of Science, Tokyo, Japan. Corresponding author: T. Itos1730170@s.tsukuba.ac.jp

the humanoid robot HRP-4C [10]. However, as that studyfocused only on reproducing the human motion trajectory, itwould be difficult to apply their method to an actuator-drivenassistive device used to powerfully support the human body.

The challenge, therefore, was to develop a control schemethat allowed the humanoid to reproduce human motions thatoperate in response to an actively supportive device. As a firststep, we proposed a “stationary torque replacement” methodto quantify the static supportive effect of active devices [11].After generating humanoid postures by converting measuredhuman motions to humanoids motions, the supportive torquewas assessed for certain given static postures. This method,which was applied to a “Muscle Suit” [7], successfullyestimated the supportive torque. Although this was the firsthumanoid-based evaluator of active devices that consideredthe whole-body balance, its application is limited to onlystatic postures. To obtain better quantitative measurementsunder more realistic conditions, we needed to estimate thesupportive torque continuously throughout the retargetedmotion. This required a control framework for reproducinghuman motion when supported by the torque-enabled activedevice.

In this research, we proposed using a novel humanoidcontroller that reproduces human motions supported by anactive device and that tracks retargeted human motions. Wefocused on devices that support the lower back since theyare the most common commercially available products inJapan. The design of the humanoid controller is based on acouple of assumptions about how humans behave in responseto a device that both physically constrains and supportsthem, namely that humans are guided to follow nominaltrajectories and exert less effort to power themselves bytaking advantage of external supportive forces. The controlscheme was realized by tracking the trajectory of measuredhuman motion with torque feedback. The control frameworkwas validated by experiments, which evaluated the supportivetorque of the powerful assistive device “Muscle Suit” [1]using the humanoid HRP-4 [12].

This paper is organized as follows: After arguing theassumptions behind the behavioral intentions of human mo-tions when supported by wearable devices, the proposednovel control scheme that is based on these assumptionsis described in Section II. The experimental validation ofthe proposed control scheme using the humanoid HRP-4 toevaluate the Muscle Suit is described in Section III beforeconcluding the paper.

2017 IEEE-RAS 17th International Conference onHumanoid Robotics (Humanoids)Birmingham, UK, November 15-17, 2017

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II. TORQUE-BASED HUMAN MOTION TRACKINGCONTROL

A. Assumptions of Human Behavior with Assistive Devices

To replicate human motions with humanoid motions, wemade the following assumptions about human motion whenattended by an active assistive device.

Assumption 1A wearable assistive device acts as a geometricconstraint to the human body in such a way thathuman motions track almost nominal trajectories.

Assumption 2By taking full advantage of the support providedby the assistive device, the user exerts less effort inmovement.

The first assumption is based on an understanding of thestructural constraints of the assistive device. Since the deviceis designed to support a typical human task such as liftingan object, human motion is likely to follow an identicaltrajectory because it is guided by the mechanical structureof the device. The motion trajectory for the humanoid robotis generated by using the retargeting method [13] from therecorded human motion. The second assumption is based onour natural response to an assistive device, namely, we exertless when we move and rely more on the device for support.Given our assumptions, we proposed a path-tracking controlbased on torque feedback (described in II-C).

B. Human Motion Retargeting for Humanoid

Our control framework needed to track a joint trajectorythat was sufficiently close to that of humans. Motion re-targeting is an oft-used technique for endowing a computergraphics character or humanoid robot with humanlike mo-tions. In our work, the tracked trajectories were designed bythe efficient motion retargeting method for humanoid robots,which is detailed elsewhere [13]. Since the method makesuse of the geometric parameters identification, it can copewith differences in body dimension between a human androbot. It can also consider physical consistency, such as jointlimitations or balancing.

In our case, since the robot needed to wear an assistivedevice while carrying weights, we set the motion retargetingprocedure as follows:

1) The motion of a human subject wearing an assistivedevice is measured by a motion capture system.

2) The simulation model of a robot is modified to reflectthe same load conditions as in the human measure-ment. The masses of the device and the carried weightsare added as the mass point models.

3) The robot motion was retargeted from the measuredhuman motion by utilizing the simulation model, ac-cording to the method provided in [13].

Finally, the retargeted motion trajectory was sent to therobot as whole joint trajectories for the tracking control,which is described below. Human motions circumscribedby the worn device were used for retargeting because webelieved it was important to evaluate the assistive torque

Fig. 1. Relationship between joint angle trajectory and a trajectoryparameter x (ex. 3DOFs ). Each value of θi(i = 1, 2, 3) is derived from theone-by-one mapping function θ(x): [θ1 θ2 θ3]t = θ(x), x0 ≤ x ≤ x1

under conditions as close to actual use. We discuss this issuelater in the conclusion.

C. Path Tracking Control with Joint Torque feedback

In order to reproduce human motion when an externalforce is applied by an assistive device, we introduced a singlecoordinate representation of multiple joints and the pathtracking control in a manner similar to the method providedin [14]. In this scheme, the retargeted motion trajectory ofthe humanoid robot was represented by a single parameterand utilized for path tracking control. The whole joint angletrajectories of a robot were represented by a single scalarparameter x, called “the trajectory parameter” in the pathcoordinate. As an example, the case of a three-dimensionaltrajectory is shown in Fig. 1. The current joint angles arerepresented by θ(x), where θ(x) is a one-by-one mappingfunction from trajectory parameter x to current joint anglesθ. Since the retargeted motion, as mentioned previously isoften given as discrete data, the mapping function can bedesigned by the trajectory interpolation.

When the joint angles of a robot are constrained under themapping function θ(x), the equation of motion of a robotcan be written as follows:

τjoint + τext = f(x) ≜ h(θ(x), θ(x), θ(x)) (1)

where τjoint and τext are the joint torque and external torque,respectively, and f(x) or h(θ(x), θ(x), θ(x)) indicate thetorque coming from the inertial, Coriolis, and gravity forcesrespectively.

Let us now define the following error torque at the joint,where it is supported by the device:

eτ ≜ τref − τjoint (2)

where τref indicates the reference joint torque. Accordingto our assumptions, τref should be zero, or some negligiblevalue, to perform the motion with less self-joint torque. Now,we regard τref as a small constant value:

eτ +K1eτ +K2eτ = 0 (3)

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where K1 and K2 (K1 > 0,K2 > 0) are feedback gains.Since τref is constant, Eq. (3) can be written as

− d

dtτjoint −K1τjoint +K2(τref − τjoint) = 0 (4)

Here, we assume that the external torque acting on therobot changes slowly with respect to the dynamics of therobot, such that τext is quasi-static and is regarded asconstant. From this assumption, τjoint can be computed fromEq. (1) as follows:

τjoint = f(x) =∂f

∂xx (5)

By substituting Eq. (5) onto Eq. (4) and utilizing Laplacianoperator s, we obtain:

−(s+K1)∂f

∂xx+K2(τref − τjoint) = 0 (6)

(7)

Then, x is represented as:

x =K2

s+K1

(∂f

∂x

)−1

(τref − τjoint) (8)

Finally, we obtain acceleration x along the path coordinateas:

x = −K1x+K2

(∂f

∂x

)−1

(τref − τjoint) (9)

If path coordinate x is updated according to Eq. (9), thefeedback control rule written in Eq. (3) will be satisfied.

III. EXPERIMENTS

This section presents the results of our experiment toverify the proposed framework. The experimental setup withthe pneumatic exoskeleton Muscle Suit and the full-sizehumanoid robot HRP-4 was first introduced in III-A. Theproposed control method was then applied to the setup. Theexperimental results of the generated motion are shown inIII-B. We then checked whether the quantitative supportivetorque of the device could be extracted as expected bycomparing it with the joint torque measured the same motionwithout wearing the device in Section III-C.

A. Experiment Setup

The Muscle Suit shown in Fig. 2 was developed byKobayashi et al. [7], [15] and commercialized by InnophysCo., Ltd., Tokyo [1]. It was designed to help humans carryheavy objects by supporting their lower back. The structureof the Muscle Suit is also shown in Fig.2. The device hasMcKibben pneumatic actuators on the backside, which aredriven by compressed air. It is designed like a backpack sothat a user can easily put on and take off the device. Thedevice is fixed and tightened by a belt at the shoulders, andthe thighs are supported by soft pads. The waist joint of theMuscle Suit consists of two joints that allow natural humanmotions. The user can control the amount of air supplied tothe pneumatic actuators using a touch switch or, in instanceswhere both hands are occupied, using an exhalation switch.

torso

waist

thigh

Muscle Suit

Mckibben

ar�ficial

Muscles

Axis 1

Axis 2

Fig. 2. Structure of Muscle Suit

Fig. 3. Humanoid HRP-4 (softcover)

Fig. 3 shows the human-sized humanoid robot HRP-4used for device evaluation in this research. Its geometricparameters, such as link lengths, are designed to matchthe values of the average Japanese female within an errorrange of 10%; its height and weight is 155 cm and 40 kg,respectively. Given that the body of the humanoid robot issimilar in dimensions to a human, it can more easily imitatehumanlike motions. The total number of degrees of freedom(DOFs) of the robot is 37. Each arm has 9 DOFs (shoulderjoint: 3, elbow: 1, wrist: 3, hand: 2), Each leg has 7 DOFs(hip joint: 3, knee: 1, ankle: 2, toe: 1), the waist has 3 DOFs,and the neck has 2 DOFs. The original hard plastic cover ofHRP-4 was replaced by soft urethane to enable it to wearthe assistive device like humans do without modification.

B. Experiment of proposed control scheme

Since the proposed controller required retargeted humanmotion trajectories, we first measured human motion with amotion capture system. The system recorded the subject as helifted a 5-kg load while wearing the Muscle Suit, as shown inFig. 5 (upper side). The subject bent down and held the 5-kgweight, then lifted the weight using the supportive force ofthe device, which was activated by the exhalation switch. Thesubject’s motion trajectory was captured by several motioncapture cameras (Motion Analysis). The joint trajectories of

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a humanoid robot were generated from the measured humanmotions using the procedure mentioned in II-B. The obtainedtrajectories were used as θ(x) in our control scheme.

In the following, we discuss the implementation of thecontroller in our experiments. Since the Muscle Suit wasdesigned to support the human lower back, we focused onthe waist joint torque of the humanoid. For real-time control,the upper-body dynamics of the robot were simplified by aquasi-static inverted pendulum model. The function f(x) inEq. (1) was formulated as follows:

f(x) = mgl sin(θwaist(x)) (10)

where θwaist is the waist joint angle, m is the total mass ofthe upper body, g is the acceleration due to gravity, and l isthe relative distance between the waist joint position and thecenter of the total mass of the upper body.

We then differentiated Eq. (10) with respect to the trajec-tory parameter x:

∂f

∂x= mgl cos(θwaist)

∂θwaist

∂x(11)

In the actual implementation, Eq. (9) was discretized. Wealso considered that τref is equal to 0 to have the robotbear down its full upper-body weight on the assistive device.Finally, Eq. (9) was reformulated as follows:

x[t+ 1] = x[t] + x[t]∆t (0 ≤ x ≤ 1)x[t] = K1x[t− 1]

−K2

(mgl cos(θwaist)

∂θwaist

∂x

)†τjoint

(12)

where, ∗† means the singular robust (SR) inverse [16]. τjointis measured by current sensor attached to the motor of thewaist joint of HRP-4.

The above controller was tested on the humanoid robotHRP-4 outfitted with the Muscle Suit. We attached a totalweight of 4-kg onto the robot (2-kg weights on each wrist).The maximum value of air pressure supplied to the devicewas set to 0.3 MPa (standard pressure supply: 0.5 MPa) forsafety precautions. In the experiment, the controller of therobot was activated when the robot was bending down, atwhich point we pressed the switch to supply air to the MuscleSuit. As the device lifted the HRP-4, the robot tracked thetrajectory of the retargeted human motion.

Snapshots of the original human motions and correspond-ing robotic motions are shown in Fig. 5. The robot wasable to lift its upper trunk while maintaining humanlikemotions very similar to the original. We also checkedwhether the actuator torque of the waist joint decreasedby benefiting from the assistive effect. Figure 4 shows theactuator torque of the waist joint and the transition of thetrajectory parameter x. The device switch was pushed ataround 3 s, robotic movement started at 4 s, and roboticmovement was completed at around 11 s. At 3−4 s, therobot maintained a starting bent posture because the device’sassistive torque could not lift the robot’s trunk. Then, theabsolute value of the actuator torque was within 10 Nmduring 6−11 s. Therefore, the proposed control frameworksuccessfully reduced the actuator torque of the robot upon

Fig. 4. Waist joint torque and velocity of parameter x (0 ≤ x ≤ 1). Therobot motion starts at 4 [sec] when the waist joint torque becomes zero.The motion is maintained for 7 s and ends at around 11 s.

lifting its upper trunk. In contrast, the torque remained atapproximately 20 Nm after stopping the motion. At the endof the motion, the velocity of the waist joint in the originalhuman motion was equal to zero. It follows that ∂θwaist

∂x , aswell as the SR inverse in Eq. (12) is usually equal to zero.When the velocity of the trajectory parameter was almostequal to zero, our controller stopped the update of trajectoryparameter. This singular occurrence at the end of our motionexperiment will be addressed in future work.

C. Evaluation of Supportive effect of the Muscle Suit

The supportive torque was derived by computing thedifference between the generated joint torques with andwithout the device. The detailed method is as follows:

1) Measure the joint torques and joint angles duringthe motion generated by the proposed control schemewhile the assistive device is activated (which is thesame procedure as in III-B).

2) Reproduce the recorded motion in Step 1 by the normaljoint servo controllers, and measure the joint torqueswithout activating the device.

3) Compute the supportive torque by subtracting thetorque measured in Step 2 from that of Step 1.

The red and blue lines indicate the torque in Step 1 andStep 2, respectively in Fig. 6. A positive value indicates thatthe torque is in a bending position at waist joint, and anegative value indicates that the torque in a straighteningup position. The gray areas in the graphs mean that thejoint angles were not updated since parameter x is equalto 0 or 1. Note that the proposed evaluation currently makessense during motion (when 0 < x < 1); on the contrary, theevaluation for static postures is detailed in [11]. There is aclear difference in trend between the two lines. During the4−10 s period, the value of the actuated torque with device’ssupport was within the 0 to 10 Nm range whereas thetorque without the support was within the −30 and −60 Nmrange. Though the controller tried to keep the waist jointtorque zero, there was a slight torque offset which causedthe robot to push the device back around the waist joint.Nonetheless, the torque was slight relative to the gravitytorque for keeping its posture. The assistive torque of the

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Fig. 5. Original human motion and retargeted humanoid motion. In human motion measurement, a human subject used the exhalation switch to activatethe assistive device (Muscle Suit), and then lifted a 5-kg weight. The device provided the supportive force while switch is turned on. In the humanoidrobot experiment, two 2-kg weights were attached to each wrist joint, and the device was activated by a switch controlled by an experiment operator. Therobot started the lifting motion when the device was activated and provided enough supportive torque.

waist joint supported by the Muscle Suit in Step 3 is shownin Fig. 7. The maximum assistive torque was 67.2 Nm, andthe average was 29.2 Nm. Since the device was activatedafter 3−4 s, the graph indicates that the device generatedhigher supportive torque when the robot bent more deeply.This result is consistent with the original characterization ofthe Muscle Suit in which the actuated force became largerwith a deeper bending angle, meaning that the pneumaticactuators were being stretched.

We also showed the actuated torque in the right and lefthips in Figs. 8 and 9, respectively. The Muscle Suit reducedthe actuated torques of both hip joints, even though thecontroller was mainly designed to reduce the actuated torqueof the waist joint.

IV. CONCLUSIONS

In this paper, we introduced a new quantitative evaluationmethod for active wearable assistive devices by using torquecontrol using path tracking. We focused on the human use ofwearable assistive devices and considered two assumptions:first, human motion is constrained by the device worn;second, human motion involves minimal exertion as it fullyexploits the assistive force. Based on those assumptions,we devised a new control scheme to track the retargeted

human motion trajectory according to the supportive torquegenerated by the device.

The proposed method was applied to a pneumaticallyactuated wearable device called a “Muscle Suit”, which wasstrapped to the humanoid HRP-4. We have shown that thehumanoid can track the given path, in accordance with theexternal force generated by the Muscle Suit, and that theproposed evaluation method enables the supportive torqueto be quantified by computing the difference between therobot’s joint torques with and without device activation.

We have shown that the supportive effect of powerfulactive devices can be quantitatively evaluated during motionby using a humanoid robot. We are also well aware of severallimitations to our approach.

First, variations in users’ size, alignment or motions shouldbe addressed in any future work. This work was based onone subject’s measured motions, it will be important toanalyze variance in several aspects over different subjectsby measuring not only human but also the device. Themotion model can be enhanced by taking into account thosevariances.

The second issue relates to the controller. The key contri-bution of proposed method is enabling evaluation of assistivetorque during motion. However, this is a simplified methodthat leaves much room for improvement. Although we have

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Fig. 6. Comparison of waist joint torque with and without support

Fig. 7. Comparison of differential torque with and without support at thewaist joint

measured human motions under assistance to be as closeto actual use, we need to go beyond the trajectory-trackingbased controller to further investigate the human controllermodel. One possibility is inverse optimal control [17] toidentify the whole-body control under external force input.

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