Perception Based Method

Perception-based methods leverage computer vision and machine learning to enable machines to "perceive" their environment, mirroring human sensory processing. Current research focuses on improving robustness and efficiency across diverse applications, employing neural networks (including CNNs, LSTMs, and ensemble models) to process visual and tactile data for tasks like autonomous navigation, human-robot interaction, and robotic manipulation. These advancements are crucial for enhancing the capabilities of autonomous systems, improving human-computer interfaces, and enabling new possibilities in fields such as industrial automation and healthcare. The ultimate goal is to create systems that can reliably interpret complex sensory information in real-time, leading to safer and more effective interactions with the physical world.