3 accepted papers
Training robots to operate effectively in environments with uncertain states—such as ambiguous object properties or unpredictable interactions—remains a longstanding challenge in robotics. Imitation learning methods typically rely on successful examples and often neglect failure scenarios where unce
Object grasping in 3D space is crucial for robotic applications. Such tasks are performed by utilizing depth map data acquired from RGB-D images or 3D point cloud data. However, these methods struggle when dealing with transparent objects, as transparency limits sensor performance when predicting de
Sufficiently perceiving the environment is a critical factor in robot motion generation. Although the introduction of deep visual processing models have contributed in extending this ability, existing methods lack in the ability to actively modify what to perceive; humans perform internally during v