A Neuromorphic Control System for Agile Biped Walking

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Rush-hour in a London mainline railway station: a passenger effortlessly walks swiftly through the swirling crowd, looking at a large screen 20 meters away, talking on a mobile phone in his left hand, holding a cup of coffee in his right hand, avoiding collision with anyone, and making his way to platform 14. This seems effortless. But from the robotics view, this is almost miraculous because all these tasks are controlled by a single brain, efficiently in parallel. To behave like this human, today's robot would have to use a large million-dollar super-computer or several connected computers using Kilowatts of energy, and the performance would still not comparable to that of a human brain. To build a robot brain by reverse engineering the human or animal brain has been the ultimate goal of many large inter-disciplinary projects in recent years. Today, the most promising technology to physically and structurally emulate the brain is neuromorphic engineering, which uses electronic circuits to mimic neuro-biological architectures. Compared with standard computer-based controllers, neuromorphic controllers are naturally parallel, more compact and more energy efficient. It is widely thought that a neuromorphic brain will be the centre of the next generation of intelligent autonomous robots. Many studies in neuromorphic engineering have developed neuromorphic systems to realize specific functional modules of the brain, e.g., hearing, vision, olfaction, cognition, and action learning. The proposed project is targeting another fundamental control function of the human brain -- bipedal (two-legged) walking. Just like humans and animals, a robot must be able to move agilely in order to execute its tasks in the natural environment. But, compared with traditional counterparts, the performance of the neuromorphically controlled legged robots (especially biped robots) is very poor in terms of versatile and agile locomotion. This is mainly because their neuromorphic circuits emulated only the basic function module of the spinal neural network, which could only realize propulsion control. In animals, propulsion control and body posture control are fully integrated, which is fundamental for their agile locomotion in a complex natural environment. Particularly, in humans, to meet the functional requirements of agile bipedal walking, the spinal neural network is heavily modulated by the supraspinal levels. However, it is still not fully understood in biology how the neuronal modules at the spinal level and supraspinal level interact with and modulate each other in the control of human bipedal locomotion. Building on the team's track record in biped robotics, neuromorphic circuit design, neuromorphic simulation, and computational neuroscience, the proposed project aims to fill this gap via developing a multi-module and multi-level (i.e., spinal level and supraspinal level) neuromorphic system. In the neuromorphic system in this project, we will implement the functions of three neuronal modules that have been known to play important roles in human locomotion control. By coupling such a neuromorphic system with a purposely designed biped robot using a new method (model-driven concurrent integration), we will be able to explore the unknown interaction/modulation mechanisms between these modules that could lead to agile biped walking.At the heart of our proposal is the ambition to make a notable step forward in the area of neuromorphic robotics. This project will, for the first time, demonstrate an agile 3D biped robot that has human-like walking patterns and a neuromorphic control mechanism.