title: Twisting Lids Off with Two Hands
summary: Manipulating objects with two multi-fingered hands has been a long-standing
  challenge in robotics, attributed to the contact-rich nature of many
  manipulation tasks and the complexity inherent in coordinating a
  high-dimensional bimanual system. In this work, we consider the problem of
  twisting lids of various bottle-like objects with two hands, and demonstrate
  that policies trained in simulation using deep reinforcement learning can be
  effectively transferred to the real world. With novel engineering insights into
  physical modeling, real-time perception, and reward design, the policy
  demonstrates generalization capabilities across a diverse set of unseen
  objects, showcasing dynamic and dexterous behaviors. Our findings serve as
  compelling evidence that deep reinforcement learning combined with sim-to-real
  transfer remains a promising approach for addressing manipulation problems of
  unprecedented complexity.
paper_authors._id: 65e75f39cd00e92129df0b5f
paper_authors.name: Toru Lin
paper_authors._id: 65e75f39cd00e92129df0b60
paper_authors.name: Zhao-Heng Yin
paper_authors._id: 65e75f39cd00e92129df0b61
paper_authors.name: Haozhi Qi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-05T21:01:48.975Z
paper_authors._id: 658871e7d1331d552be8b457
paper_authors.avatarUrl: /avatars/c323178ad1e0fbdfa5e66dcd4ba357e6.svg
paper_authors.fullname: Haozhi Qi
paper_authors.type: user
paper_authors.user: HaozhiQi
paper_authors._id: 65e75f39cd00e92129df0b62
paper_authors.name: Pieter Abbeel
paper_authors._id: 65e75f39cd00e92129df0b63
paper_authors.name: Jitendra Malik
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-05T21:02:02.344Z
paper_authors._id: 65369a95605a07338de78ab0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/sGFjOjLT2akN-sn5beVWL.jpeg
paper_authors.fullname: Jitendra Malik 
paper_authors.type: user
paper_authors.user: jitendra1995
paper_title: Twisting Lids Off with Two Hands
paper_summary: Manipulating objects with two multi-fingered hands has been a long-standing
  challenge in robotics, attributed to the contact-rich nature of many
  manipulation tasks and the complexity inherent in coordinating a
  high-dimensional bimanual system. In this work, we consider the problem of
  twisting lids of various bottle-like objects with two hands, and demonstrate
  that policies trained in simulation using deep reinforcement learning can be
  effectively transferred to the real world. With novel engineering insights into
  physical modeling, real-time perception, and reward design, the policy
  demonstrates generalization capabilities across a diverse set of unseen
  objects, showcasing dynamic and dexterous behaviors. Our findings serve as
  compelling evidence that deep reinforcement learning combined with sim-to-real
  transfer remains a promising approach for addressing manipulation problems of
  unprecedented complexity.
paper_ai_keywords: deep reinforcement learning sim-to-real transfer