Weakly Supervised Correspondence Learning
Zihan Wang,Zhangjie Cao,Yilun Hao,Dorsa Sadigh,Zihan Wang,Zhangjie Cao,Yilun Hao,Dorsa Sadigh
Correspondence learning is a fundamental problem in robotics, which aims to learn a mapping between state, action pairs of agents of different dynamics or embodiments. However, current correspondence learning methods either leverage strictly paired data-which are often difficult to collect-or learn in an unsupervised fashion from unpaired data using regularization techniques such as cycle-consiste...
