Development and Training of a Neural Controller for Hind Leg Walking in a Dog Robot

Animals dynamically adapt to varying terrain and small perturbations with remarkable ease. These adaptations arise from complex interactions between the environment and biomechanical and neural components of the animal’s body and nervous system. Research into mammalian locomotion has resulted in several neural and neuro-mechanical models, some of which have been tested in simulation, but few “synthetic nervous systems” have been implemented in physical hardware models of animal systems. One reason is that the implementation into a physical system is not straightforward. For example, it is difficult to make robotic actuators and sensors that model those in the animal. Therefore, even if the sensorimotor circuits were known in great detail, those parameters would not be applicable and new parameter values must be found for the network in the robotic model of the animal. This manuscript demonstrates an automatic method for setting parameter values in a synthetic nervous system composed of non-spiking leaky integrator neuron models. This method works by first using a model of the system to determine required motor neuron activations to produce stable walking. Parameters in the neural system are then tuned systematically such that it produces similar activations to the desired pattern determined using expected sensory feedback. We demonstrate that the developed method successfully produces adaptive locomotion in the rear legs of a dog-like robot actuated by artificial muscles. Furthermore, the results support the validity of current models of mammalian locomotion. This research will serve as a basis for testing more complex locomotion controllers and for testing specific sensory pathways and biomechanical designs. Additionally, the developed method can be used to automatically adapt the neural controller for different mechanical designs such that it could be used to control different robotic systems

Stepping Responses to Treadmill Perturbations vary with Severity of Motor Deficits in Human SCI

In this study, we investigated the responses to tread perturbations during human stepping on a treadmill. Our approach was to test the effects of perturbations to a single leg using a split-belt treadmill in healthy participants and in participants with varying severity of spinal cord injury (SCI). We recruited 11 people with incomplete SCI and 5 noninjured participants. As participants walked on an instrumented treadmill, the belt on one side was stopped or accelerated briefly during mid to late stance. A majority of participants initiated an unnecessary swing when the treadmill was stopped in mid stance, although the likelihood of initiating a step was decreased in participants with more severe SCI. Accelerating or decelerating one belt of the treadmill during stance altered the characteristics of swing. We observed delayed swing initiation when the belt was decelerated (i.e. the hip was in a more flexed position at time of swing) and advanced swing initiation with acceleration (i.e. hip extended at swing initiation). Further, the timing and leg posture of heel strike appeared to remain constant, reflected by a sagittal plane hip angle at heel strike that remained the same regardless of the perturbation. In summary, our results supported the current understanding of the role of sensory feedback and central drive in the control of stepping in participants with incomplete SCI and noninjured participants. In particular, the observation of unnecessary swing during a stop perturbation highlights the interdependence of central and sensory drive in walking control

From Biological Synapses to "Intelligent" Robots

This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is brought forward. Learning without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival-relevant or task-relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for “intelligent” robotics and sensor systems. Keywords: Hebbian learning; synaptic plasticity; neural networks; self-organization; brain; reinforcement; sensory processing; robot contro