Walking and running is notoriously difficult to recreate in robots. Now, a group of researchers has overcome some of these challenges by creating an innovative method that employs central pattern generators -- neural circuits located in the spinal cord that generate rhythmic patterns of muscle activity -- with deep reinforcement learning.
The method not only imitates walking and running motions but also generates movements for frequencies where motion data is absent, enables smooth transition movements from walking to running, and allows for adapting to environments with unstable surfaces.
DRL is one way researchers have sought to overcome this. DRL extends traditional reinforcement learning by leveraging deep neural networks to handle more complex tasks and learn directly from raw sensory inputs, enabling more flexible and powerful learning capabilities. Its disadvantage is the huge computational cost of exploring vast input space, especially when the system has a high degree of freedom.
CPGs are neural circuits located in the spinal cord that, like a biological conductor, generate rhythmic patterns of muscle activity. In animals, a reflex circuit works in tandem with CPGs to provide adequate feedback that allows them to adjust their speed and walking/running movements to suit the terrain.
The research group comprised members from Tohoku University's Graduate School of Engineering and the École Polytechnique Fédérale de Lausanne, or the Swiss Federal Institute of Technology in Lausanne.A team of researchers has developed a new method for controlling lower limb exoskeletons using deep reinforcement learning. The method enables more robust and natural walking control for users of ...
Engineering Electricity Robotics Research Neural Interfaces Artificial Intelligence Robotics Computer Modeling
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