Closed-loop Long-horizon Robotic Planning via Equilibrium Sequence
  Modeling
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.
66fe1f1215148a910cc0df1c
Jinghan Li
admin_assigned
2024-10-03T09:27:14.588Z
669fd314e43472e6109c7a88
/avatars/6c7290d0394694b12344c13e2fc5a03e.svg
lijinghan
user
ljh0104
66fe1f1215148a910cc0df1d
Zhicheng Sun
admin_assigned
2024-10-03T09:27:01.992Z
62fc758172a7ab50b4b89c8c
https://cdn-avatars.huggingface.co/v1/production/uploads/1677251053045-62fc758172a7ab50b4b89c8c.jpeg
Zhicheng Sun
user
feifeiobama
66fe1f1215148a910cc0df1e
Fei Li
66fe1f1215148a910cc0df1f
Cao Sheng
66fe1f1215148a910cc0df20
Jiazhong Yu
66fe1f1215148a910cc0df21
Yadong Mu
Closed-loop Long-horizon Robotic Planning via Equilibrium Sequence
  Modeling
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.
A self-refining equilibrium planner is developed for autonomous robotic task planning, enhancing performance and scalability through iterative refinement and nested equilibrium sequence modeling.
task planning high-level task descriptions action sequences language model agents self-refining scheme equilibrium end-to-end optimization supervised learning nested equilibrium sequence modeling closed-loop planning feedback VirtualHome-Env benchmark inference computation