Generalizable Long-Horizon Manipulations with Large Language Models
Haoyu Zhou Mingyu Ding Weikun Peng Masayoshi Tomizuka Lin Shao Chuang Gan
This work introduces a framework harnessing the capabilities of Large
Language Models (LLMs) to generate primitive task conditions for generalizable
long-horizon manipulations with novel objects and unseen tasks. These task
conditions serve as guides for the generation and adjustment of Dynamic
Movement Primitives (DMP) trajectories for long-horizon task execution. We
further create a challenging robotic manipulation task suite based on Pybullet
for long-horizon task evaluation. Extensive experiments in both simulated and
real-world environments demonstrate the effectiveness of our framework on both
familiar tasks involving new objects and novel but related tasks, highlighting
the potential of LLMs in enhancing robotic system versatility and adaptability.
Project website: https://object814.github.io/Task-Condition-With-LLM/