Closed-loop Long-horizon Robotic Planning via Equilibrium Sequence
  Modeling
Authors: name: Jinghan Li, name: Zhicheng Sun, name: Fei Li, name: Cao Sheng, name: Jiazhong Yu, name: Yadong Mu

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.