3 accepted papers
Adapting the mastered manipulation skill to novel objects is still challenging for robots. Recent works have attempted to endow the robot with the ability to adapt to unseen tasks by leveraging meta-learning. However, these methods are data-hungry in the training phase, which limits their applicatio
Humans can quickly determine the force required to grasp a deformable object to prevent its sliding or excessive deformation through vision and touch, which is still a challenging task for robots. To address this issue, we propose a novel 3D convolution-based visual-tactile fusion deep neural networ…
Predicting whether a particular grasp will succeed is critical to performing stable grasping and manipulating tasks. Robots need to combine vision and touch as humans do to accomplish this prediction. The primary problem to be solved in this process is how to learn effective visual-tactile fusion fe