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MIT CSAILAjman UniversityArbaminch University EthiopiaAssam UniversityNehru College of Engineering and Research CentreFeb 8, 2025This paper introduces an Adaptable Reinforcement Learning-oriented Multifaceted Data Combination (AdRL-MDC) system to train a robotic hand for gaming, aiming to improve accuracy and consistency in motion management. The system integrates an adaptable training process for ensemble classification, a reinforcement learning paradigm for robot intelligence, and a multifaceted data combination framework. Experimental results demonstrate that the CNN-based ensemble framework achieves high accuracy with efficient computation, and the depth vision-oriented CNN classification algorithm attains 100% recognition accuracy.
A novel system enables robotic hands to achieve perfect motion recognition in games by fusing CNN-based vision with adaptable reinforcement learning.
The use of artificial intelligence (AI) in robotics, particularly in the advancement of gaming robotics, has drawn a lot of interest as technological and scientific developments continue to advance. This innovation opens up new possibilities for creating smart autonomous robots for gaming. Nevertheless, there is more work to be done to achieve great accuracy and consistency in motion management. The challenges of the robotics' level of smart and the motion-capturing structure's capacity to prevent interruption still require resolution, although numerous methods have been put out to improve monitoring accuracy by merging various data. This study proposes an adaptable reinforcement learning (RL)-oriented multifaceted data combination (AdRL-MDC) system for training a robotic hand to play games alongside humans. It incorporates an adaptable training process for updating the ensemble classification algorithm, an RL paradigm that provides the robots with smart knowledge, and a multifaceted data combination framework that is resistant to interruption. The following studies demonstrate the AdRL-MDC system's previously indicated functionality. The ensemble framework, which combines a convolutional neural network (CNN), performs well in terms of accuracy and computing duration. Furthermore, the depth vision-oriented CNN classification algorithm achieves one hundred percent recognition accuracy, implying that the forecasted motions are the real value.
