VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models
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Cited by 147 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
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 vision-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. Videos and code at https://voxposer.github.io
Key facts. Cited by 733 papers in the map (data through 7 Oct 2026). Builds on 107 of its 146 references that are in the map. First posted on arXiv 12 Jul 2023.
Builds on
107 of 146 references.
References · 107
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Citing papers per year: 2023: 56; 2024: 215; 2025: 212; 2026: 250.
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