3 accepted papers
Instance grasping, which aims to grasp a specific object out of clutter, is a fundamental task within robotics. However, allowing a robot to quickly learn to perform instance grasping for new, previously unseen objects remains challenging. In this work, we present an instance grasping meta-learning
Despite significant advances in few-shot classification, object detection, or speech recognition in recent years, training an effective robot to adapt to previously unseen environments in a small data regime is still a long-lasting problem for learning from demonstrations (LfD). A promising solution
Although robots have been widely applied in various fields, allowing a robot to perform a wide range of tasks like humans is a significant challenge. One promising method is meta-learning, which enables robots to learn from demonstrations with the concept of “learning to learn.” Howeve