title: Closed-loop Long-horizon Robotic Planning via Equilibrium Sequence
    Modeling
summary: In the endeavor to make autonomous robots take actions, task planning is a
  major challenge that requires translating high-level task descriptions into
  long-horizon action sequences. Despite recent advances in language model
  agents, they remain prone to planning errors and limited in their ability to
  plan ahead. To address these limitations in robotic planning, we advocate a
  self-refining scheme that iteratively refines a draft plan until an equilibrium
  is reached. Remarkably, this process can be optimized end-to-end from an
  analytical perspective without the need to curate additional verifiers or
  reward models, allowing us to train self-refining planners in a simple
  supervised learning fashion. Meanwhile, a nested equilibrium sequence modeling
  procedure is devised for efficient closed-loop planning that incorporates
  useful feedback from the environment (or an internal world model). Our method
  is evaluated on the VirtualHome-Env benchmark, showing advanced performance
  with better scaling for inference computation. Code is available at
  https://github.com/Singularity0104/equilibrium-planner.
paper_authors._id: 66fe1f1215148a910cc0df1c
paper_authors.name: Jinghan Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-03T09:27:14.588Z
paper_authors._id: 669fd314e43472e6109c7a88
paper_authors.avatarUrl: /avatars/6c7290d0394694b12344c13e2fc5a03e.svg
paper_authors.fullname: lijinghan
paper_authors.type: user
paper_authors.user: ljh0104
paper_authors._id: 66fe1f1215148a910cc0df1d
paper_authors.name: Zhicheng Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-03T09:27:01.992Z
paper_authors._id: 62fc758172a7ab50b4b89c8c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1677251053045-62fc758172a7ab50b4b89c8c.jpeg
paper_authors.fullname: Zhicheng Sun
paper_authors.type: user
paper_authors.user: feifeiobama
paper_authors._id: 66fe1f1215148a910cc0df1e
paper_authors.name: Fei Li
paper_authors._id: 66fe1f1215148a910cc0df1f
paper_authors.name: Cao Sheng
paper_authors._id: 66fe1f1215148a910cc0df20
paper_authors.name: Jiazhong Yu
paper_authors._id: 66fe1f1215148a910cc0df21
paper_authors.name: Yadong Mu
paper_title: Closed-loop Long-horizon Robotic Planning via Equilibrium Sequence
    Modeling
paper_summary: In the endeavor to make autonomous robots take actions, task planning is a
  major challenge that requires translating high-level task descriptions into
  long-horizon action sequences. Despite recent advances in language model
  agents, they remain prone to planning errors and limited in their ability to
  plan ahead. To address these limitations in robotic planning, we advocate a
  self-refining scheme that iteratively refines a draft plan until an equilibrium
  is reached. Remarkably, this process can be optimized end-to-end from an
  analytical perspective without the need to curate additional verifiers or
  reward models, allowing us to train self-refining planners in a simple
  supervised learning fashion. Meanwhile, a nested equilibrium sequence modeling
  procedure is devised for efficient closed-loop planning that incorporates
  useful feedback from the environment (or an internal world model). Our method
  is evaluated on the VirtualHome-Env benchmark, showing advanced performance
  with better scaling for inference computation. Code is available at
  https://github.com/Singularity0104/equilibrium-planner.
paper_ai_keywords: self-refining scheme equilibrium end-to-end analytical perspective supervised learning nested equilibrium sequence modeling closed-loop planning environment internal world model VirtualHome-Env benchmark