Improving Naturalness of Locomotion of Many-Muscle...

Improving Naturalness of Locomotion of Many-Muscle Humanoids Ri Yu * Dongchul Jo † Jehee Lee ‡ Seoul National University Figure 1: Simulation of normal walk with muscle activation minimization. 1 Introduction For many decades, researchers have worked on the simulation of biped locomotion such as human walking. As simulation models have evolved, the simulation using a musculoskeletal model has also become possible [Lee et al. 2014]. Since the number of muscles of models they use is greater than the number of DoF of the models, the optimization problem is undetermined. In order to solve this problem, they use the 2-norm of muscle activations as an objective of optimization and minimize it. However, to obtain more realistic simulation results, real mechanisms of human movement should be applied. In spite of development of the humanoid locomotion simulation, it is still not natural enough due to the lack of knowledge in the mechanisms of human movement. Discussions about this topic have been made, and many people believe that human movement tries to minimize one of the followings; muscle activation, derivatives of muscle activation, joint torque, derivatives of joint torque, metabolic energy expenditure or some combination of these. In this study, we did ex- periments for each minimization case and compared the results of kinematic data and energy consumption. 2 Our Approach We used many-muscle controller and a simulator presented by Lee et al.[2014] They simulated biped locomotion using the musculoskeletal model. To control locomotion of the musculoskeletal models, they used muscle contraction dynamics and quadratic programming. In quadratic programming, they minimized muscle activation. Firstly, we conducted an experiment under joint torque minimization, and compared the result with activation minimization case. * e-mail:[email protected] † e-mail:[email protected] ‡ e-mail:[email protected] There was no noticeable difference on kinematic data. One major difference was the amount of energy consumptions during the simulation. Energy consumption of joint torque minimization case was higher than that of activation minimization case. Secondly, we did an experiment with metabolic energy expenditure minimization. We designed metabolic energy expenditure as an activation weighted by the muscle mass. In case of muscle activation minimization, all the muscles treated as they have same muscle size. By weighting with their mass, we could consider muscle forces which is proportional to muscle size in optimization process. There was no signiﬁcant difference on kinematic data between activation and metabolic energy expenditure minimization cases as well. Energy consumption of this case was higher than that of muscle activation case but lower than that of joint torque case. When muscle activation minimization case, muscles of a model is relaxed completely. However, this is not the same as human movement because it is known that human generate redundant energy(muscle co-activation) to keep the body stable, which ap- pears in joint torque and metabolic energy expenditure minimization cases. In joint torque minimization case, because only joint torque is minimized, muscle co-activation can be generated. In metabolic energy expenditure minimization case, a combination of minor muscle forces compensates some portion of major muscle force, so redundant activation can appear. Consequently, we are going to model the muscle co-activation considering organic re- lationships between muscles, rather than simply minimizing some objective. To do so, we will ﬁnd weighted sum of these objectives or add new objective representing co-activation. References LEE, Y., PARK, M. S., KWON, T., AND LEE, J. 2014. Locomotion control for many-muscle humanoids. ACM Trans. Graph. 33,6 (Nov.), 218:1–218:11.

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Transcript of Improving Naturalness of Locomotion of Many-Muscle...

Improving Naturalness of Locomotion of Many-Muscle Humanoids

Ri Yu∗ Dongchul Jo † Jehee Lee‡

Seoul National University

Figure 1: Simulation of normal walk with muscle activation minimization.

1 Introduction

For many decades, researchers have worked on the simulation ofbiped locomotion such as human walking. As simulation mod-els have evolved, the simulation using a musculoskeletal modelhas also become possible [Lee et al. 2014]. Since the number ofmuscles of models they use is greater than the number of DoF ofthe models, the optimization problem is undetermined. In order tosolve this problem, they use the 2-norm of muscle activations as anobjective of optimization and minimize it. However, to obtain morerealistic simulation results, real mechanisms of human movementshould be applied.

In spite of development of the humanoid locomotion simulation, itis still not natural enough due to the lack of knowledge in the mech-anisms of human movement. Discussions about this topic have beenmade, and many people believe that human movement tries to mini-mize one of the followings; muscle activation, derivatives of muscleactivation, joint torque, derivatives of joint torque, metabolic energyexpenditure or some combination of these. In this study, we did ex-periments for each minimization case and compared the results ofkinematic data and energy consumption.

2 Our Approach

We used many-muscle controller and a simulator presented by Leeet al.[2014] They simulated biped locomotion using the muscu-loskeletal model. To control locomotion of the musculoskeletalmodels, they used muscle contraction dynamics and quadratic pro-gramming. In quadratic programming, they minimized muscle ac-tivation.

Firstly, we conducted an experiment under joint torque minimiza-tion, and compared the result with activation minimization case.

∗e-mail:[email protected]†e-mail:[email protected]‡e-mail:[email protected]

There was no noticeable difference on kinematic data. One majordifference was the amount of energy consumptions during the sim-ulation. Energy consumption of joint torque minimization case washigher than that of activation minimization case.

Secondly, we did an experiment with metabolic energy expendi-ture minimization. We designed metabolic energy expenditure asan activation weighted by the muscle mass. In case of muscle acti-vation minimization, all the muscles treated as they have same mus-cle size. By weighting with their mass, we could consider muscleforces which is proportional to muscle size in optimization process.There was no significant difference on kinematic data between ac-tivation and metabolic energy expenditure minimization cases aswell. Energy consumption of this case was higher than that of mus-cle activation case but lower than that of joint torque case.

When muscle activation minimization case, muscles of a modelis relaxed completely. However, this is not the same as humanmovement because it is known that human generate redundantenergy(muscle co-activation) to keep the body stable, which ap-pears in joint torque and metabolic energy expenditure minimiza-tion cases. In joint torque minimization case, because only jointtorque is minimized, muscle co-activation can be generated. Inmetabolic energy expenditure minimization case, a combination ofminor muscle forces compensates some portion of major muscleforce, so redundant activation can appear. Consequently, we aregoing to model the muscle co-activation considering organic re-lationships between muscles, rather than simply minimizing someobjective. To do so, we will find weighted sum of these objectivesor add new objective representing co-activation.

References

LEE, Y., PARK, M. S., KWON, T., AND LEE, J. 2014. Locomotioncontrol for many-muscle humanoids. ACM Trans. Graph. 33, 6(Nov.), 218:1–218:11.