Controlling articulated robots in task-space with spiking silicon neurons

摘要

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.