Continual Learning for 6-DoF Grasp Synthesis via Experience and Demonstrations
This method introduces a continual learning framework for 6-DoF grasp synthesis that adapts online by updating grasp scores and recalling user demonstrations without retraining the network. It uses a memory-based scorer in an embedding space to rank candidate grasps based on accumulated experience and human input, allowing robots to improve performance on novel objects during deployment.