title: Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon
    Manipulation
summary: Many real-world manipulation tasks consist of a series of subtasks that are
  significantly different from one another. Such long-horizon, complex tasks
  highlight the potential of dexterous hands, which possess adaptability and
  versatility, capable of seamlessly transitioning between different modes of
  functionality without the need for re-grasping or external tools. However, the
  challenges arise due to the high-dimensional action space of dexterous hand and
  complex compositional dynamics of the long-horizon tasks. We present Sequential
  Dexterity, a general system based on reinforcement learning (RL) that chains
  multiple dexterous policies for achieving long-horizon task goals. The core of
  the system is a transition feasibility function that progressively finetunes
  the sub-policies for enhancing chaining success rate, while also enables
  autonomous policy-switching for recovery from failures and bypassing redundant
  stages. Despite being trained only in simulation with a few task objects, our
  system demonstrates generalization capability to novel object shapes and is
  able to zero-shot transfer to a real-world robot equipped with a dexterous
  hand. More details and video results could be found at
  https://sequential-dexterity.github.io
paper_authors._id: 64f833a25515d7dcce9a5a88
paper_authors.name: Yuanpei Chen
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2023-09-06T08:09:07.170Z
paper_authors._id: 62677c4cba5c48f85fde15ca
paper_authors.avatarUrl: /avatars/7e5fe4f2948de32c27a432fb26074a88.svg
paper_authors.fullname: Chen Wang
paper_authors.type: user
paper_authors.user: jeremyz
paper_authors._id: 64f833a25515d7dcce9a5a89
paper_authors.name: Chen Wang
paper_authors._id: 64f833a25515d7dcce9a5a8a
paper_authors.name: Li Fei-Fei
paper_authors._id: 64f833a25515d7dcce9a5a8b
paper_authors.name: C. Karen Liu
paper_title: Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon
    Manipulation
paper_summary: Many real-world manipulation tasks consist of a series of subtasks that are
  significantly different from one another. Such long-horizon, complex tasks
  highlight the potential of dexterous hands, which possess adaptability and
  versatility, capable of seamlessly transitioning between different modes of
  functionality without the need for re-grasping or external tools. However, the
  challenges arise due to the high-dimensional action space of dexterous hand and
  complex compositional dynamics of the long-horizon tasks. We present Sequential
  Dexterity, a general system based on reinforcement learning (RL) that chains
  multiple dexterous policies for achieving long-horizon task goals. The core of
  the system is a transition feasibility function that progressively finetunes
  the sub-policies for enhancing chaining success rate, while also enables
  autonomous policy-switching for recovery from failures and bypassing redundant
  stages. Despite being trained only in simulation with a few task objects, our
  system demonstrates generalization capability to novel object shapes and is
  able to zero-shot transfer to a real-world robot equipped with a dexterous
  hand. More details and video results could be found at
  https://sequential-dexterity.github.io
paper_ai_summary: A reinforcement learning system, Sequential Dexterity, chains dexterous policies to achieve complex, long-horizon manipulation tasks, demonstrating generalization and zero-shot transfer to real-world robots.
paper_ai_keywords: reinforcement learning dexterous policies transition feasibility function chaining success rate policy-switching generalization zero-shot transfer