Advanced biomimetic robotic hand with EMG lifelong learning and recognition.

The design and implementation of a suitable robotic hand for a toddler-sized humanoid robot is a challenging task. The main purpose of this work is to optimize the design of an anthropomorphic robotic hand and control it by using surface electromyographic (sEMG) signals. Isolation forest backward particle swarm optimization is used to optimize the robotic hand. The fitness function is defined by thumb opposability and the ability to grasp objects based on grasp taxonomy. Learning without forgetting (LWF) is adopted to train sEMG signal data sequentially, and the consequently learned model is used as an ensemble to control the optimized robotic hand. Webots is adopted to simulate the scenario of grasping objects to optimize the design of the hand. The optimized robotic hand is compared with two robotic hands, and the highest fitness values in the simulator and real world are obtained. Three different sEMG inputs, namely, raw data, bandpass, and discrete wavelet transformed bandpass, are compared in LWF, and the structure of neural networks is considered. The final LWF model is successfully applied to a real-world system to manipulate a robotic hand via hand gesture classification in real time.