KETO: Learning Keypoint Representations for Tool Manipulation
Zengyi Qin,Kuan Fang,Yuke Zhu,Li Fei-Fei,Silvio Savarese,Zengyi Qin,Kuan Fang,Yuke Zhu,Li Fei-Fei,Silvio Savarese
We aim to develop an algorithm for robots to manipulate novel objects as tools for completing different task goals. An efficient and informative representation would facilitate the effectiveness and generalization of such algorithms. For this purpose, we present KETO, a framework of learning keypoint representations of tool-based manipulation. For each task, a set of task-specific keypoints is joi...
