A multizone cerebellar chip for bioinspired adaptive robot control and sensorimotor processing

Adaptive Filter Model of Cerebellum for Biological Muscle Control With Spike Train Inputs

Prior applications of the cerebellar adaptive filter model have included a range of tasks within simulated and robotic systems. However, this has been limited to systems driven by continuous signals. Here, the adaptive filter model of the cerebellum is applied to the control of a system driven by spiking inputs by considering the problem of controlling muscle force. The performance of the standard adaptive filter algorithm is compared with the algorithm with a modified learning rule that minimizes inputs and a simple proportional-integral-derivative (PID) controller. Control performance is evaluated in terms of the number of spikes, the accuracy of spike input locations, and the accuracy of muscle force output. Results show that the cerebellar adaptive filter model can be applied without change to the control of systems driven by spiking inputs. The cerebellar algorithm results in good agreement between input spikes and force outputs and significantly improves on a PID controller. Input minimization can be used to reduce the number of spike inputs, but at the expense of a decrease in accuracy of spike input location and force output. This work extends the applications of the cerebellar algorithm and demonstrates the potential of the adaptive filter model to be used to improve functional electrical stimulation muscle control.

Understanding brain functional architecture through robotics

Robotics is increasingly seen as a useful test bed for computational models of the brain functional architecture underlying animal behavior. We provide an overview of past and current work, focusing on probabilistic and dynamical models, including approaches premised on the free energy principle, situating this endeavor in relation to evidence that the brain constitutes a layered control system. We argue that future neurorobotic models should integrate multiple neurobiological constraints and be hybrid in nature.

PhotoElasticFinger: Robot Tactile Fingertip Based on Photoelastic Effect

The sense of touch is fundamental for a one-to-one mapping between the environment and a robot that physically interacts with the environment. Herein, we describe a tactile fingertip design that can robustly detect interaction forces given data collected from a camera. This design is based on the photoelastic effect observed in silicone matter. Under the force applied to the silicone rubber, owing to the stress-induced birefringence, the light propagating within the silicone rubber is subjected to the angular phase shift, where the latter is proportional to the increase in the image brightness in the camera frames. We present the calibration and test results of the photoelastic sensor design on a bench using a robot arm and with a certified industrial force torque sensor. We also discuss the applications of this sensor design and its potential relationship with human mechano-transduction receptors. We achieved a force sensing range of up to 8 N with a force resolution of around 0.5 N. The photoelastic tactile fingertip is suitable for robot grasping and might lead to further progress in robust tactile sensing.