The cerebellum, composed of numerous neurons, plays various roles in motor control. Although it is functionally subdivided, the cerebellar cortex has a canonical structural pattern in neuronal circuits including a recurrent circuit pattern formed by granule cells (GrCs) and Golgi cells (GoCs). The canonical circuital pattern suggests the existence of a fundamental computational algorithm, although it remains unclear. Modeling and simulation studies are useful for verifying hypotheses about complex systems. Previous models have shown that they could reproduced the neurophysiological data of the cerebellum; however, the dynamic characteristics of the system have not been fully elucidated. Understanding the dynamic characteristics of the circuital pattern is necessary to reveal the computational algorithm embedded in the circuit. This study conducted numerical simulations using the cerebellar circuit model to investigate dynamic characteristics in a simplified model of cerebellar microcircuits. First, the diversity and chaoticity of the patterns of spike trains generated from GrCs depending on the synaptic strength between the GrCs and GoCs were investigated based on cluster analysis and the Lyapunov exponent, respectively. Then the effect of synaptic strength on learning tasks was investigated based on the convergence properties of the output signals from Purkinje cells. The synaptic strength for high learning performance was almost consistent with that for the high diversity of the generated patterns and the edge of chaos. These results suggest that the learning performance of the cerebellar circuit depends on the diversity and the chaoticity of the spiking patterns from the GrC-GoC recurrent circuit.

Legged robots have excellent terrestrial mobility for traversing diverse environments and, thus, have the potential to be deployed in a wide variety of scenarios. However, they are susceptible to falling and leg malfunction during locomotion. Although the use of a large number of legs can overcome these problems, it makes the body long and leads to many legs being constrained to contact with the ground to support the long body, which impedes maneuverability. To improve the locomotion maneuverability of such robots, this study focuses on dynamic instability, which induces rapid and large movement changes, and uses a 12-legged robot with a flexible body axis. Our previous work found that the straight walk of the robot becomes unstable through a Hopf bifurcation when the body-axis flexibility is changed, which induces body undulations. Furthermore, we developed a simple controller based on the Hopf bifurcation and showed that the instability facilitates the turning of the robot. In this study, we newly found that the straight walk becomes unstable through a pitchfork bifurcation when the body-axis flexibility is changed in a way different from that in our previous work. In addition, the pitchfork bifurcation induces a transition into a curved walk, whose curvature can be controlled by the body-axis flexibility. We developed a simple controller based on the pitchfork bifurcation characteristics and demonstrated that the robot can perform a turning maneuver superior to that with the previous controller. This study provides a novel design principle for maneuverable locomotion of many-legged robots using intrinsic dynamic properties.

In this paper, we proposed a control system with learning to investigate neuromechanical functions for generating adaptive bipedal locomotion on a splitbelt treadmill. Humans show two types of adaptations, called early adaptation and late adaptation, in splitbelt treadmill walking. In our previous work, we investigated the locomotor behavior of a biped robot driven by nonlinear oscillators with phase resetting and showed that it produced the early adaptation like humans. However, because the locomotion control system did not contain any learning mechanism, it did not show the late adaptation. In this paper, we newly develop learning systems, which modulate the interlimb and intralimb coordination patterns, and incorporate them to our locomotion control system. We investigated the locomotor behavior of a biped robot using computer simulations and robot experiments. The results showed the early and late adaptations during the splitbelt treadmill walking and the time evolution of locomotion parameters was similar to that of humans, which might contribute to understanding of adaptive mechanism in humans and to guiding principle for designing a control system of biped robots.

Although physiological studies have shown evidence of phase resetting during fictive locomotion, the functional roles of phase resetting in actual locomotion remain largely unclear. In this paper, we have constructed a control system for a biped robot based on physiological findings and investigated the functional roles of phase resetting in the gait transition from quadrupedal to bipedal locomotion by numerical simulations and experiments. So far, although many studies have investigated methods to achieve stable locomotor behaviors for various gait patterns of legged robots, their transitions have not been thoroughly examined. Especially, the gait transition from quadrupedal to bipedal requires drastic changes in the robot posture and the reduction of the number of supporting limbs, and therefore, the stability greatly changes during the transition. Thus, this transition poses a challenging task. We constructed a locomotion control system using an oscillator network model based on a two-layer hierarchical network model of a central pattern generator while incorporating the phase resetting mechanism and created robot motions for the gait transition based on the physiological concept of synergies. Our results, which demonstrate that phase resetting increases the robustness in gait transition, will contribute to the understanding of the phase resetting mechanism in biological systems and lead to a guiding principle to design control systems for legged robots.

In this paper, we present an experimental validation of a novel decentralized passivity-based control strategy for teleoperating a group of Unmanned Aerial Vehicles (UAVs): the slave side, consisting of the UAVs, is endowed with large group autonomy by allowing time-varying topology and interrobot/obstacle collision avoidance. The master side, represented by a human operator, controls the group motion and receives suitable force feedback cues informing her/him about the remote slave motion status. Passivity theory is exploited for guaranteeing stability of the slave side and of the overall teleoperation channel. Results of experiments involving the use of 4 quadcopters are reported and discussed, confirming the soundness of the paper theoretical claims.

In this paper, we investigate the locomotor behaviors of a biped robot on a splitbelt treadmill using a locomotion-control system composed of nonlinear oscillators with phase resetting. Our results show that the robot establishes stable walking on the treadmill at various speeds of the belts due to modulation of the rhythm and phase by phase resetting. In addition, the phase differences between the leg movements shifted from out of phase, and duty factors were autonomously modulated depending on the speed discrepancy between the belts occurring through dynamic interactions among the robot's mechanical system, the oscillator control system, and the environment. Such shifts of phase differences between the leg movements and modulations of duty factors are observed during human splitbelt treadmill walking, and our results suggest that our dynamic model using the robot and oscillator control system reflects a certain essence of the ability to produce adaptive locomotor behaviors.