A Joint-Space Humanoid Robot Torso Motion Planning Method Based on Spiking Neural Networks

Through long-term evolution, humans have developed extensive experience in adjusting body posture using the torso to reduce physical effort and create a comfortable workspace for arms. Imitation learning can efficiently transfer the human experience to robots. Therefore, we propose a joint-space torso motion planning network (TMPNet) for our humanoid upper-body robot to imitate the motion strategy between the human torso and dual arms, thereby guiding the robot torso motion. Compared to several mainstream inverse kinematics methods, TMPNet can enhance dual-arm flexibility through torso motion, compensate for the head’s degrees of freedom, and avoid sudden large angle changes and high iterative computation costs. To further reduce the robot’s energy consumption, a brain-inspired edge computer equipped with a neuromorphic chip is selected as the hardware computing platform. In addition, as the spiking neural network with its spike emission mechanism can fully leverage the neuromorphic chip’s advantages, it is employed to implement TMPNet.