title: DexArt: Benchmarking Generalizable Dexterous Manipulation with
    Articulated Objects
summary: To enable general-purpose robots, we will require the robot to operate daily
  articulated objects as humans do. Current robot manipulation has heavily relied
  on using a parallel gripper, which restricts the robot to a limited set of
  objects. On the other hand, operating with a multi-finger robot hand will allow
  better approximation to human behavior and enable the robot to operate on
  diverse articulated objects. To this end, we propose a new benchmark called
  DexArt, which involves Dexterous manipulation with Articulated objects in a
  physical simulator. In our benchmark, we define multiple complex manipulation
  tasks, and the robot hand will need to manipulate diverse articulated objects
  within each task. Our main focus is to evaluate the generalizability of the
  learned policy on unseen articulated objects. This is very challenging given
  the high degrees of freedom of both hands and objects. We use Reinforcement
  Learning with 3D representation learning to achieve generalization. Through
  extensive studies, we provide new insights into how 3D representation learning
  affects decision making in RL with 3D point cloud inputs. More details can be
  found at https://www.chenbao.tech/dexart/.
paper_authors._id: 645c61732e8e7c269fd944df
paper_authors.name: Chen Bao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-15T08:52:10.086Z
paper_authors._id: 676050fb1d80fb3c2083b9b7
paper_authors.avatarUrl: /avatars/f5cdcb9fb026d53b796a4c9b5864927d.svg
paper_authors.fullname: Chen Bao
paper_authors.type: user
paper_authors.user: Kami-code
paper_authors._id: 645c61732e8e7c269fd944e0
paper_authors.name: Helin Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T14:45:00.013Z
paper_authors._id: 647988332b688f67eec58f62
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/P24GTakbHzWOHHj0KksoZ.jpeg
paper_authors.fullname: Helin Xu
paper_authors.type: user
paper_authors.user: HelinXu
paper_authors._id: 645c61732e8e7c269fd944e1
paper_authors.name: Yuzhe Qin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T14:45:09.328Z
paper_authors._id: 64765e133d8d1cb7918f494d
paper_authors.avatarUrl: /avatars/17dd8e5920a21c90bdb55f193cc01d88.svg
paper_authors.fullname: Yuzhe Qin
paper_authors.type: user
paper_authors.user: yzqin
paper_authors._id: 645c61732e8e7c269fd944e2
paper_authors.name: Xiaolong Wang
paper_title: DexArt: Benchmarking Generalizable Dexterous Manipulation with
    Articulated Objects
paper_summary: To enable general-purpose robots, we will require the robot to operate daily
  articulated objects as humans do. Current robot manipulation has heavily relied
  on using a parallel gripper, which restricts the robot to a limited set of
  objects. On the other hand, operating with a multi-finger robot hand will allow
  better approximation to human behavior and enable the robot to operate on
  diverse articulated objects. To this end, we propose a new benchmark called
  DexArt, which involves Dexterous manipulation with Articulated objects in a
  physical simulator. In our benchmark, we define multiple complex manipulation
  tasks, and the robot hand will need to manipulate diverse articulated objects
  within each task. Our main focus is to evaluate the generalizability of the
  learned policy on unseen articulated objects. This is very challenging given
  the high degrees of freedom of both hands and objects. We use Reinforcement
  Learning with 3D representation learning to achieve generalization. Through
  extensive studies, we provide new insights into how 3D representation learning
  affects decision making in RL with 3D point cloud inputs. More details can be
  found at https://www.chenbao.tech/dexart/.
paper_ai_keywords: Dexterous manipulation Articulated objects Multi-finger robot hand DExArt Benchmark Physical simulator Manipulation tasks Generalizability Learned policy Degrees of freedom Reinforcement Learning (RL) 3D representation learning 3D point cloud inputs