Closed-loop Long-horizon Robotic Planning via Equilibrium Sequence Modeling
Jinghan Li, Zhicheng Sun, Fei Li, Cao Sheng, Jiazhong Yu, Yadong Mu
2024-10-03
Summary
This paper discusses a new method called HarmoniCa, which improves how robots plan their actions over long periods by better aligning the training and execution processes.
What's the problem?
Robots need to plan their actions based on high-level tasks, but current methods often lead to mistakes and are not good at planning for the future. This is especially important for tasks that require a series of steps over time. The existing systems have trouble because they don't effectively use past information when making decisions, which can slow them down and lead to errors.
What's the solution?
HarmoniCa addresses these problems by introducing a self-refining planning system that improves its initial plans through a process of continuous adjustment. This method allows robots to learn from previous steps (called equilibrium states) and use that knowledge to make better decisions in real-time. The authors developed a new training method that helps the robot understand how to use past information effectively and designed a way to balance the quality of the final outcome with how well it uses its memory of past actions.
Why it matters?
This research is important because it enhances the capabilities of robots in performing complex tasks over longer periods. By improving how robots plan and adapt their actions, HarmoniCa can lead to more efficient and reliable robotic systems, which could be applied in various fields such as automation, manufacturing, and even personal assistance.
Abstract
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.