Seungmoon Song

A neural circuitry that emphasizes spinal feedback generates diverse behaviours of human locomotion

It is often assumed that central pattern generators, which generate rhythmic patterns without rhythmic inputs, play a key role in the spinal control of human locomotion. We propose a neural control model in which the spinal control generates muscle stimulations mainly through integrated reflex pathways with no central pattern generator. Using a physics‐based neuromuscular human model, we show that this control network is sufficient to compose steady and transitional 3‐D locomotion behaviours, including walking and running, acceleration and deceleration, slope and stair negotiation, turning, and deliberate obstacle avoidance. The results suggest feedback integration to be functionally more important than central pattern generation in human locomotion across behaviours. In addition, the proposed control architecture may serve as a guide in the search for the neurophysiological origin and circuitry of spinal control in humans.

Regulating speed and generating large speed transitions in a neuromuscular human walking model

Although current humanoid controllers can rely on inverse kinematics or dynamics of the full humanoid system, powered prosthetic legs or assistive devices cannot, because they do not have access to the full states of the human system. This limitation creates the need for alternative control strategies. One strategy is to embed fundamental knowledge about legged dynamics and control in local feedback. In a previous paper, we have developed a control model of human locomotion which relies mostly on local feedback. The model can robustly walk at normal walking speeds. Here we extend this model to adapt to a wide range of walking speeds and to generate corresponding speed transitions. We use optimization of the models control parameters and find key parameters responsible for steady walking between 0.8ms-1 and 1.8ms-1, covering the range of speed at which humans normally walk. Using these parameters, we demonstrate speed transitions between slow and fast walking. In addition, we discuss how the speed-dependent changes of the identified control parameters connect to biped walking dynamics, and suggest how these changes can be integrated in local feedback control.

The energetic cost of adaptive feet in walking

While humanoid feet are made of rigid plates, human feet have evolved into highly articulated and flexible elements. This adaptiveness provides key advantages. It absorbs impacts and secures grip when interacting with the environment. However, the human foot design potentially increases the energetic cost, because it features actuators and provides less power transfer than a rigid plate does. Here we use neuromuscular models with different foot designs and show that human feet incur about 20% more energetic cost than rigid ones for walking speeds up to 1.2ms−1, which is close to the preferred walking speed. Above this speed, human feet do not show an energetic disadvantage. In addition we propose a foot design for prosthetic or humanoid feet which preserves key features of adaptive feet but does not require actuation, and show that it reduces the energetic cost by 15% or more independent of the walking speed. We conclude that human evolution may have traded the advantages of adaptive feet for energy efficiency, and that robotic systems could gain the former without compromising on the latter.

Integration of an adaptive swing control into a neuromuscular human walking model

Understanding the neuromuscular control underlying human locomotion has the potential to deliver practical controllers for humanoid and prosthetic robots. However, neurocontrollers developed in forward dynamic simulations are seldom applied as practical controllers due to their lack of robustness and adaptability. A key element for robust and adaptive locomotion is swing leg placement. Here we integrate a previously identified robust swing leg controller into a full neuromuscular human walking model and demonstrate that the integrated model has largely improved behaviors including walking on very rough terrain (±10cm) and stair climbing (15cm stairs). These initial results highlight the potential of the identified robust swing control. We plan to generalize it to a range of human locomotion behaviors critical in rehabilitation robotics.

The effect of foot compliance encoded in the windlass mechanism on the energetics of human walking

The human foot, which is the part of the body that interacts with the environment during locomotion, consists of rich biomechanical design. One of the unique designs of human feet is the windlass mechanism. In a previous simulation study, we found that the windlass mechanism seems to improve the energy efficiency of walking. To better understand the origin of this efficiency, we here conduct both simulation and experimental studies exploring the influence of foot compliance, which is one of the functionalities that the windlass mechanism embeds, on the energetics of walking. The studies show that walking with compliant feet incurs more energetic costs than walking with stiff feet. The preliminary results suggest that the energy saved by introducing the windlass mechanism does not originate from the compliance it embeds. We speculate that the energy savings of the windlass mechanism are related more to its contribution to reducing the effective foot length in swing than to providing compliance in stance.

Development of a bipedal robot that walks like an animation character

Our goal is to bring animation characters to life in the real world. We present a bipedal robot that looks like and walks like an animation character. We start from animation data of a character walking. We develop a bipedal robot which corresponds to lower part of the character following its kinematic structure. The links are 3D printed and the joints are actuated by servo motors. Using trajectory optimization, we generate an open-loop walking trajectory that mimics the characters walking motion by modifying the motion such that the Zero Moment Point stays in the contact convex hull. The walking is tested on the developed hardware system.

Development of an Omnidirectional Walking Engine for Full-Sized Lightweight Humanoid Robots

In this paper, we propose and demonstrate an omnidirectional walking engine that achieves stable walking using feedback from an inertial measurement unit (IMU). The 3D linear inverted pendulum model (3D-LIPM) is used as a simplified model of the robot, the zero moment point (ZMP) criterion is used as the stability criterion, and only the feedback from the IMU is utilized for stabilization. The proposed walking engine consists of two parts; the omnidirectional gait generator, and the stability controller. ZMP equations, derived based on the 3D-LIPM, are used in the omnidirectional gait generator. The solutions of the differential equations are directly used which reduces the computation cost compare to other existing methods. Two kinds of feedback controllers are implemented for the stability controller; one is the indirect reference ZMP controller, and the other is the indirect joint controller. The walking engine is tested on a lightweight, full-sized, 21-degree-of-freedom (DOF) humanoid robot CHARLI-L (Cognitive Humanoid Autonomous Robot with Learning Intelligence, version Lightweight) which stands 141 cm tall and weighs only 12.7 kg. The design goals of CHARLI-L are low development cost, lightweight, and simple design, which all match well with the proposed walking engine. The results of the experiments present the efficacy of our approach.Copyright

Evaluation of a Neuromechanical Walking Control Model Using Disturbance Experiments

Neuromechanical simulations have been used to study the spinal control of human locomotion which involves complex mechanical dynamics. So far, most neuromechanical simulation studies have focused on demonstrating the capability of a proposed control model in generating normal walking. As many of these models with competing control hypotheses can generate human-like normal walking behaviors, a more in-depth evaluation is required. Here, we conduct the more in-depth evaluation on a spinal-reflex-based control model using five representative gait disturbances, ranging from electrical stimulation to mechanical perturbation at individual leg joints and at the whole body. The immediate changes in muscle activations of the model are compared to those of humans across different gait phases and disturbance magnitudes. Remarkably similar response trends for the majority of investigated muscles and experimental conditions reinforce the plausibility of the reflex circuits of the model. However, the models responses lack in amplitude for two experiments with whole body disturbances suggesting that in these cases the proposed reflex circuits need to be amplified by additional control structures such as location-specific cutaneous reflexes. A model that captures these selective amplifications would be able to explain both steady and reactive spinal control of human locomotion. Neuromechanical simulations that investigate hypothesized control models are complementary to gait experiments in better understanding the control of human locomotion.

Generalization of a muscle-reflex control model to 3D walking

The neural controller that generates human locomotion can currently not be measured directly, and researchers often resort to forward dynamic simulations of the human neuromuscular system to propose and test different controller architectures. However, most of these models are restricted to locomotion in the sagittal plane, which limits the ability to study and compare proposed neural controls for 3D-related motions. Here we generalize a previously identified reflex control model for sagittal plane walking to 3D locomotion. The generalization includes additional degrees of freedom at the hips in the lateral plane, their actuation and control by hip abductor and adductor muscles, and 3D compliant ground contact dynamics. The resulting 3D model of human locomotion generates normal walking while producing human-like ground reaction forces and moments, indicating that the proposed neural controller based on muscle reflexes generalizes well to 3D locomotion.

Toward a virtual neuromuscular control for robust walking in bipedal robots

Walking controllers for bipedal robots have not yet reached human levels of robustness in locomotion. Imitating the human motor control might be an alternative strategy for generating robust locomotion in robots. We seek to control bipedal robots with a specific neuromuscular human walking model proposed previously. Here, we present a virtual neuromuscular controller, VNMC, that emulates this neuromuscular model to generate desired motor torques for a bipedal robot. We test the VNMC on a high-fidelity simulation of the ATRIAS bipedal robot constrained to the sagittal plane. We optimize the control parameters to tolerate maximum ground-height changes, which resulted in ATRIAS walking on a terrain with up to ±7 cm height changes. We further evaluate the robustness of the optimized controller to external and internal disturbances. The optimized VNMC adapts to 90% of random terrains with ground-height changes up to ±2 cm. It endures 95% of ±30 Ns horizontal pushes on the trunk, and 90% of 8 Ns backward and 4 Ns forward impulses on the swing foot throughout the gait cycle. Furthermore, the VNMC is resilient to modeling errors and sensor noise much larger than the equivalent uncertainties in the real robot. The results suggest VNMC as a potential alternative to generate robust locomotion in bipedal robots.