Controlling articulated robots in task-space with spiking silicon neurons

Emulating how humans coordinate articulated limbs within the brain´s power budget promises to accelerate progress in building autonomous biomimetic robots. Here, we used a neuromorphic approach - low-power analog silicon spiking neurons - to control an articulated robot in real-time. We obtained a closed-form control function that computes robot motor torques given the robot´s joint angles (state) and desired end-effector forces; factorized the function into a set of sub-functions over five unique three-dimensional domains; and regressed each sub-function on to the steady-state spiking responses of one out of five silicon spiking-neuron pools. The spiking pools controlled a three degree-of-freedom robot´s motor torques in real-time and performed reaches to arbitrary locations in space with less than 2 cm root-mean-square trajectory tracking error (of an analytical controller). The controller is compliant and can draw shapes with a pen on a dynamically perturbed surface while remaining stable. Using force control resulted in linear responses to perturbations in end-effector coordinates (task-space), which effectively filtered noise due to neuron spikes. Factorizing the controller reduced the neural regression´s complexity to cubic in the dynamic range of the robot´s state and desired forces. Doing so made acquiring spiking responses for regression tractable in time (~2-3 min), and enabled reliable trajectory tracking with only 1280 neurons. This is the first time a neuromorphic system has achieved realtime manipulation for an articulated robot with three or more degrees-of-freedom.