Robot Perception

Robot perception research aims to equip robots with robust and efficient ways to understand their environment through various sensor modalities, enabling safe and effective interaction. Current efforts focus on improving the accuracy and speed of perception using techniques like Bayesian frameworks, deep learning models (including transformers and neural networks), and multimodal data fusion (combining visual, acoustic, tactile, and inertial data). These advancements are crucial for enabling more sophisticated robotic applications in diverse fields, such as manufacturing, healthcare, and agriculture, by enhancing robots' ability to navigate, manipulate objects, and collaborate with humans.