Large language models (LLMs) are shown to possess a wealth of actionable knowledge that can be extracted for robot manipulation in the form of reasoning and planning. Despite the progress, most still rely on pre-defined motion primitives to carry out the physical interactions with the environment, which remains a major bottleneck. In this work, we aim to synthesize robot trajectories, i.e., a dense sequence of 6-DoF end-effector waypoints, for a large variety of manipulation tasks given an open-set of instructions and an open-set of objects. We achieve this by first observing that LLMs excel at inferring affordances and constraints given a free-form language instruction. More importantly, by leveraging their code-writing capabilities, they can interact with a visual-language model (VLM) to compose 3D value maps to ground the knowledge into the observation space of the agent. The composed value maps are then used in a model-based planning framework to zero-shot synthesize closed-loop robot trajectories with robustness to dynamic perturbations. We further demonstrate how the proposed framework can benefit from online experiences by efficiently learning a dynamics model for scenes that involve contact-rich interactions. We present a large-scale study of the proposed method in both simulated and real-robot environments, showcasing the ability to perform a large variety of everyday manipulation tasks specified in free-form natural language. Project website: https://voxposer.github.io

翻译：大型语言模型（LLMs）被证明拥有丰富的可操作知识，可通过推理与规划形式提取用于机器人操作。尽管取得进展，多数方法仍依赖预定义的运动基元与环境进行物理交互，这仍是主要瓶颈。本研究旨在为开放式指令集和物体集下的多种操作任务合成机器人轨迹，即密集的6自由度末端执行器路径点序列。我们首先观察到，LLMs擅长从自由形式语言指令中推断可供性与约束条件。更重要的是，通过利用其代码编写能力，它们能与视觉语言模型（VLM）交互，组合出3D价值地图，将知识锚定到智能体的观测空间。组合后的价值地图被用于基于模型的规划框架中，零样本合成闭环机器人轨迹，并具备对动态扰动的鲁棒性。我们进一步展示了所提框架如何通过高效学习涉及接触丰富交互场景的动力学模型，从在线经验中获益。我们在模拟和真实机器人环境中开展了大规模研究，展示了执行由自由形式自然语言指定的各种日常操作任务的能力。项目网站：https://voxposer.github.io