Resonant hopping of a robot controlled by an artificial neural oscillator

"The bouncing gaits of terrestrial animals (hopping, running, trotting) can be modeled as a hybrid dynamic system, with spring-mass dynamics during stance and ballistic motion during the aerial phase. We used a simple hopping robot controlled by an artificial neural oscillator to test the ability of the neural oscillator to adaptively drive this hybrid dynamic system. The robot had a single joint, actuated by an artificial pneumatic muscle in series with a tendon spring. We examined how the oscillator-robot system responded to variation in two neural control parameters: descending neural drive and neuromuscular gain. We also tested the ability of the oscillator-robot system to adapt to variations in mechanical properties by changing the series and parallel spring stiffnesses. Across a 100-fold variation in both supraspinal gain and muscle gain, hopping frequency changed by less than 10%. The neural oscillator consistently drove the system at the resonant half-period for the stance phase, and adapted to a new resonant half-period when the muscle series and parallel stiffnesses were altered. Passive cycling of elastic energy in the tendon accounted for 70-79% of the mechanical work done during each hop cycle. Our results demonstrate that hopping dynamics were largely determined by the intrinsic properties of the mechanical system, not the specific choice of neural oscillator parameters. The findings provide the first evidence that an artificial neural oscillator will drive a hybrid dynamic system at partial resonance."http://deepblue.lib.umich.edu/bitstream/2027.42/64204/1/bb8_2_026001.pd

Uniqueness of human running coordination: The integration of modern and ancient evolutionary innovations

Running is a pervasive activity across human cultures and a cornerstone of contemporary health, fitness and sporting activities. Yet for the overwhelming predominance of human existence running was an essential prerequisite for survival. A means to hunt, and a means to escape when hunted. In a very real sense humans have evolved to run. Yet curiously, perhaps due to running’s cultural ubiquity and the natural ease with which we learn to run, we rarely consider the uniqueness of human bipedal running within the animal kingdom. Our unique upright, single stance, bouncing running gait imposes a unique set of coordinative difficulties. Challenges demanding we precariously balance our fragile brains in the very position where they are most vulnerable to falling injury while simultaneously retaining stability, steering direction of travel, and powering the upcoming stride: all within the abbreviated time-frames afforded by short, violent ground contacts separated by long flight times. These running coordination challenges are solved through the tightly-integrated blending of primitive evolutionary legacies, conserved from reptilian and vertebrate lineages, and comparatively modern, more exclusively human, innovations. The integrated unification of these top-down and bottom-up control processes bestows humans with an agile control system, enabling us to readily modulate speeds, change direction, negotiate varied terrains and to instantaneously adapt to changing surface conditions. The seamless integration of these evolutionary processes is facilitated by pervasive, neural and biological, activity-dependent adaptive plasticity. Over time, and with progressive exposure, this adaptive plasticity shapes neural and biological structures to best cope with regularly imposed movement challenges. This pervasive plasticity enables the gradual construction of a robust system of distributed coordinated control, comprised of processes that are so deeply collectively entwined that describing their functionality in isolation obscures their true irrevocably entangled nature. Although other species rely on a similar set of coordinated processes to run, the bouncing bipedal nature of human running presents a specific set of coordination challenges, solved using a customized blend of evolved solutions. A deeper appreciation of the foundations of the running coordination phenomenon promotes conceptual clarity, potentially informing future advances in running training and running-injury rehabilitation interventions

Modeling discrete and rhythmic movements through motor primitives: a review

Rhythmic and discrete movements are frequently considered separately in motor control, probably because different techniques are commonly used to study and model them. Yet the increasing interest in finding a comprehensive model for movement generation requires bridging the different perspectives arising from the study of those two types of movements. In this article, we consider discrete and rhythmic movements within the framework of motor primitives, i.e., of modular generation of movements. In this way we hope to gain an insight into the functional relationships between discrete and rhythmic movements and thus into a suitable representation for both of them. Within this framework we can define four possible categories of modeling for discrete and rhythmic movements depending on the required command signals and on the spinal processes involved in the generation of the movements. These categories are first discussed in terms of biological concepts such as force fields and central pattern generators and then illustrated by several mathematical models based on dynamical system theory. A discussion on the plausibility of theses models concludes the wor

Neural Control of Interlimb Coordination and Gait Timing in Bipeds and Quadrupeds

1) A large body of behavioral data conceming animal and human gaits and gait transitions is simulated as emergent properties of a central pattern generator (CPG) model. The CPG model incorporates neurons obeying Hodgkin-Huxley type dynamics that interact via an on-center off-surround anatomy whose excitatory signals operate on a faster time scale than their inhibitory signals. A descending cornmand or arousal signal called a GO signal activates the gaits and controL their transitions. The GO signal and the CPG model are compared with neural data from globus pallidus and spinal cord, among other brain structures. 2) Data from human bimanual finger coordination tasks are simulated in which anti-phase oscillations at low frequencies spontaneously switch to in-phase oscillations at high frequencies, in-phase oscillations can be performed both at low and high frequencies, phase fluctuations occur at the anti-phase in-phase transition, and a "seagull effect" of larger errors occurs at intermediate phases. When driven by environmental patterns with intermediate phase relationships, the model's output exhibits a tendency to slip toward purely in-phase and anti-phase relationships as observed in humans subjects. 3) Quadruped vertebrate gaits, including the amble, the walk, all three pairwise gaits (trot, pace, and gallop) and the pronk are simulated. Rapid gait transitions are simulated in the order--walk, trot, pace, and gallop--that occurs in the cat, along with the observed increase in oscillation frequency. 4) Precise control of quadruped gait switching is achieved in the model by using GO-dependent modulation of the model's inhibitory interactions. This generates a different functional connectivity in a single CPG at different arousal levels. Such task-specific modulation of functional connectivity in neural pattern generators has been experimentally reported in invertebrates. Phase-dependent modulation of reflex gain has been observed in cats. A role for state-dependent modulation is herein predicted to occur in vertebrates for precise control of phase transitions from one gait to another. 5) The primary human gaits (the walk and the run) and elephant gaits (the amble and the walk) are sirnulated. Although these two gaits are qualitatively different, they both have the same limb order and may exhibit oscillation frequencies that overlap. The CPG model simulates the walk and the run by generating oscillations which exhibit the same phase relationships. but qualitatively different waveform shapes, at different GO signal levels. The fraction of each cycle that activity is above threshold quantitatively distinguishes the two gaits, much as the duty cycles of the feet are longer in the walk than in the run. 6) A key model properly concerns the ability of a single model CPG, that obeys a fixed set of opponent processing equations to generate both in-phase and anti-phase oscillations at different arousal levels. Phase transitions from either in-phase to anti-phase oscillations, or from anti-phase to in-phase oscillations, can occur in different parameter ranges, as the GO signal increases.Air Force Office of Scientific Research (90-0128, F49620-92-J-0225, 90-0175); National Science Foundation (IRI-90-24877); Office of Naval Research (N00014-92-J-1309); Army Research Office (DAAL03-88-K-0088); Advanced Research Projects Agency (90-0083

Chaotic exploration and learning of locomotion behaviours

We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage

CPU-less robotics: distributed control of biomorphs

Traditional robotics revolves around the microprocessor. All well-known demonstrations of sensory guided motor control, such as jugglers and mobile robots, require at least one CPU. Recently, the availability of fast CPUs have made real-time sensory-motor control possible, however, problems with high power consumption and lack of autonomy still remain. In fact, the best examples of real-time robotics are usually tethered or require large batteries. We present a new paradigm for robotics control that uses no explicit CPU. We use computational sensors that are directly interfaced with adaptive actuation units. The units perform motor control and have learning capabilities. This architecture distributes computation over the entire body of the robot, in every sensor and actuator. Clearly, this is similar to biological sensory- motor systems. Some researchers have tried to model the latter in software, again using CPUs. We demonstrate this idea in with an adaptive locomotion controller chip. The locomotory controller for walking, running, swimming and flying animals is based on a Central Pattern Generator (CPG). CPGs are modeled as systems of coupled non-linear oscillators that control muscles responsible for movement. Here we describe an adaptive CPG model, implemented in a custom VLSI chip, which is used to control an under-actuated and asymmetric robotic leg

Robotics and neuroscience: A rhythmic interaction

At the crossing between motor control neuroscience and robotics system theory, the paper presents a rhythmic experiment that is amenable both to handy laboratory implementation and simple mathematical modeling. The experiment is based on an impact juggling task, requiring the coordination of two upper-limb effectors and some phase-locking with the trajectories of one or several juggled objects. We describe the experiment, its implementation and the mathematical model used for the analysis. Our underlying research focuses on the role of sensory feedback in rhythmic tasks. In a robotic implementation of our experiment, we study the minimum feedback that is required to achieve robust control. A limited source of feedback, measuring only the impact times, is shown to give promising results. A second field of investigation concerns the human behavior in the same impact juggling task. We study how a variation of the tempo induces a transition between two distinct control strategies with different sensory feedback requirements. Analogies and differences between the robotic and human behaviors are obviously of high relevance in such a flexible setup

Locomotion grows up: The neuromechanical control of interlimb coordinating mechanisms in crayfish

Locomotion requires many dynamic interactions between organism and environment at several levels. It is not known how the nervous system controls all of these relationships to ultimately produce and guide locomotor behavior. Furthermore, it is not known whether the nervous system needs to recognize and control all of the possible body-environment interactions. In this study the crayfish (Procambarus clarkii) is used as a model system to test how size influences locomotor behavior and how a single, simplified neuromechanical system can accommodate these changes.;A set of behavioral experiments was conducted to characterize kinematics of freely walking juvenile crayfish to compare with adults. The purpose of these studies was to determine how crayfish adapt to a great change in size during their ontogeny. Juvenile and adult crayfish show differences in limb function and coordination. Although crayfish are decapods, the juveniles predominantly use the posterior legs and behave more like four-legged walkers. The difference in locomotor behavior can best be explained by differences in chelae size. Allometric relationships between juveniles and adults show limb and body morphologies scale proportionately. Adult chelae, or claws, are twice as long and contribute ∼20% more to the total body mass in fully mature crayfish. This increase in chelae size shifts the location of the center of mass anterior as crayfish grow. The result is a change in relative load distribution that appears to affect individual limb behavior and interlimb coordination. Shifting the center of mass in adults by amputating the chelae resulted in limb behavior and interlimb coordination more similar to that observed in juveniles. Likewise, applying load to the rostrum of juveniles altered behavior and changed limb function in the posterior legs similar to adults with large chelae. The results of these experiments suggest that crayfish of all sizes adapt to changes in load distribution by adjusting behavior of individual legs.;To test whether developmental influences have an effect on walking behavior, juveniles were induced to walk on a treadmill at various speeds. The animals showed more consistent limb coordination as walking speed increased, similar to adults. Selected legs were then amputated to test how gait was affected. Amputating legs removes sensory feedback from the distal leg to the central nervous system. The behavior of the stump is therefore more representative of the endogenous rhythmicity of the central pattern generator (CPG). Juveniles showed no differences in coordination in individual legs. Coordination between adjacent ipsilateral legs was also the same as that observed in adults following amputation. Furthermore, intact legs acquired new interlimb coordination similar to adults. These results suggested that juvenile and adult crayfish have functionally similar nervous systems controlling walking.;Finally, a 3-D virtual crayfish was built to test whether differences in walking between juveniles and adults could be due to mechanical influences alone. The model crayfish lacked direct connections between legs. The model responded to shifts in the center of mass by showing more consistent limb coordination in those legs nearest the center of mass. This was achieved through indirect mechanical coupling of the legs through the environment and body of the crayfish. This mechanism also produced realistic adaptive behavior when limbs were amputated. This showed that differences between adult and juvenile walking are due solely to mechanical influences associated with the changing center of mass as the animals grow. These results suggest further that organisms do not need high levels of control to produce coordinated behavior. Locomotor behavior arises through interactions between body, limb, and environment that are a function of the spatio-temporal dynamics of body morphology. The results may be applicable to a large number of walking systems