Summary Background Repeated periods of stimulation of the spinal cord and training seems to have amplified the ability to consciously control movement. Methods An individual three years post C7-T1 subluxation presented with a complete loss of clinically detectable voluntary motor function and partial preservation of sensation below the T1 cord segment. Following 170 locomotor training sessions, a 16-electrode array was surgically placed on the dura (L1-S1 cord segments) to allow for chronic electrical stimulation. After implantation and throughout stand retraining with epidural stimulation, 29 experiments were performed. Extensive stimulation combinations and parameters were tested to achieve standing and stepping. Findings Epidural stimulation enabled the human lumbosacral spinal circuitry to dynamically elicit full weight-bearing standing with assistance provided only for balance for 4·25 minutes in a subject with a clinically motor complete SCI. This occurred when using stimulation at parameters optimized for standing while providing bilateral load-bearing proprioceptive input. Locomotor-like patterns were also observed when stimulation parameters were optimized for stepping. In addition, seven months after implantation, the subject recovered supraspinal control of certain leg movements, but only during epidural stimulation. Interpretation Even after a severe low cervical spinal injury, the neural networks remaining within the lumbosacral segments can be reactivated into functional states so that it can recognize specific details of ensembles of sensory input to the extent that it can serve as the source of neural control. In addition, newly formed supraspinal input to this same lumbosacral segments can re-emerge as another source of control. Task specific training with epidural stimulation may have reactivated previously silent spared neural circuits or promoted plasticity. This suggests that these interventions could be a viable clinical approach for functional recovery after severe paralysis. Funding National Institutes of Health and Christopher and Dana Reeve Foundation.
This paper uses geometric methods to study basic problems in the mechanics and control of locomotion. We consider in detail the case of "undulatory locomotion" in which net motion is generated by coupling internal shape changes with external nonholonomic con straints. Such locomotion problems have a natural geometric inter pretation as a connection on a principal fiber bundle. The properties of connections lead to simplified results for studying both dynamics and issues of controllability for locomotion systems. We demonstrate the utility of this approach using a novel "snakeboard" and a mul tisegmented serpentine robot that is modeled after Hirose's active cord mechanism.
Abstract-This paper combines wavelet transforms with basic detection theory to develop a new unsupervised method for robustly detecting and localizing spikes in noisy neural recordings. The method does not require the construction of templates, or the supervised setting of thresholds. We present extensive Monte Carlo simulations, based on actual extracellular recordings, to show that this technique surpasses other commonly used methods in a wide variety of recording conditions. We further demonstrate that falsely detected spikes corresponding to our method resemble actual spikes more than the false positives of other techniques such as amplitude thresholding. Moreover, the simplicity of the method allows for nearly real-time execution.
Absfract-This paper presents novel and efficient kinematic modeling techniques for "hyper-redundant" robots. This approach is based on a "backbone curve" that captures the robot's macroscopic geometric features. The inverse kinematic, or "hyper-redundancy resolution," problem reduces to determining the time varying backbone curve behavior. To efficiently solve the inverse kinematics problem, we introduce a "modal" approach, in which a set of intrinsic backbone curve shape functions are restricted to a modal form. The singularities of the modal approach, modal non-degeneracy conditions, and modal switching are considered. For discretely segmented morphologies, we introduce "fitting" algorithms that determine the actuator displacements that cause the discrete manipulator to adhere to the backbone curve. These techniques are demonstrated with planar and spatial mechanism examples. They have also been implemented on a 30 degree-of-freedom robot prototype.
Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process. Real world systems would realistically fail or break before an optimal controller can be learned. To address this issue, we propose a controller architecture that combines (1) a model-free RL-based controller with (2) model-based controllers utilizing control barrier functions (CBFs) and (3) online learning of the unknown system dynamics, in order to ensure safety during learning. Our general framework leverages the success of RL algorithms to learn high-performance controllers, while the CBF-based controllers both guarantee safety and guide the learning process by constraining the set of explorable polices. We utilize Gaussian Processes (GPs) to model the system dynamics and its uncertainties. Our novel controller synthesis algorithm, RL-CBF, guarantees safety with high probability during the learning process, regardless of the RL algorithm used, and demonstrates greater policy exploration efficiency. We test our algorithm on (1) control of an inverted pendulum and (2) autonomous carfollowing with wireless vehicle-to-vehicle communication, and show that our algorithm attains much greater sample efficiency in learning than other state-of-the-art algorithms and maintains safety during the entire learning process.
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