Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon
  Manipulation
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
64f833a25515d7dcce9a5a88
Yuanpei Chen
extracted_pending
2023-09-06T08:09:07.170Z
62677c4cba5c48f85fde15ca
/avatars/7e5fe4f2948de32c27a432fb26074a88.svg
Chen Wang
user
jeremyz
64f833a25515d7dcce9a5a89
Chen Wang
64f833a25515d7dcce9a5a8a
Li Fei-Fei
64f833a25515d7dcce9a5a8b
C. Karen Liu
Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon
  Manipulation
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
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.
reinforcement learning dexterous policies transition feasibility function chaining success rate policy-switching generalization zero-shot transfer