RLAfford: End-to-End Affordance Learning for Robotic Manipulation
Yiran Geng,Boshi An,Haoran Geng,Yuanpei Chen,Yaodong Yang,Hao Dong,Yiran Geng,Boshi An,Haoran Geng,Yuanpei Chen,Yaodong Yang,Hao Dong
Learning to manipulate 3D objects in an interactive environment has been a challenging problem in Reinforcement Learning (RL). In particular, it is hard to train a policy that can generalize over objects with different semantic categories, diverse shape geometry and versatile functionality. In this study, we focused on the contact information in manipulation processes, and proposed a unified repre...
