DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objects

We propose DexArt, a task suite of Dexterous manipulation with Articulated object using point cloud observation. We experiment with extensive benchmark methods that learn category-level manipulation policy on seen objects. We evaluate the policies’ generalizability on a collection of unseen objects, as well as their robustness to camera viewpoint change.