Octopus: Embodied Vision-Language Programmer from Environmental Feedback
On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models
Abstract (arXiv)
Large vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning. When integrated into an embodied agent, existing embodied VLM works either output detailed action sequences at the manipulation level or only provide plans at an abstract level, leaving a gap between high-level planning and real-world manipulation. To bridge this gap, we introduce Octopus, an embodied vision-language programmer that uses executable code generation as a medium to connect planning and manipulation. Octopus is designed to 1) proficiently comprehend an agent's visual and textual task objectives, 2) formulate intricate action sequences, and 3) generate executable code. To facilitate Octopus model development, we introduce OctoVerse: a suite of environments tailored for benchmarking vision-based code generators on a wide spectrum of tasks, ranging from mundane daily chores in simulators to sophisticated interactions in complex video games such as Grand Theft Auto (GTA) and Minecraft. To train Octopus, we leverage GPT-4 to control an explorative agent that generates training data, i.e., action blueprints and corresponding executable code. We also collect feedback that enables an enhanced training scheme called Reinforcement Learning with Environmental Feedback (RLEF). Through a series of experiments, we demonstrate Octopus's functionality and present compelling results, showing that the proposed RLEF refines the agent's decision-making. By open-sourcing our simulation environments, dataset, and model architecture, we aspire to ignite further innovation and foster collaborative applications within the broader embodied AI community.
Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 60 of its 102 references that are in the map. First posted on arXiv 12 Oct 2023.
Builds on
60 of 102 references.
Closest ancestors · 3
Same direction, newest first
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- 2023EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought24 May
- 2023PaLM-E: An Embodied Multimodal Language Model6 Mar
Other references · 57
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- 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
- 2016OpenAI Gymcited by 1,684
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- 2022Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videoscited by 338
- 2017Embodied Question Answeringcited by 335
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- 2023Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memorycited by 288
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- 2023Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Modelcited by 115
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- 2018Building Generalizable Agents with a Realistic and Rich 3D Environmentcited by 109
- 2023STEVE-1: A Generative Model for Text-to-Behavior in Minecraftcited by 95
- 2023Embodied Task Planning with Large Language Modelscited by 84
- 2020An Exploration of Embodied Visual Explorationcited by 56
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Citing papers per year: 2023: 6; 2024: 27; 2025: 30; 2026: 10.
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- 2026Insight-V++: Towards Advanced Long-Chain Visual Reasoning with Multimodal Large Language Models18 Mar 2026
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- 2025Schema-Guided Scene-Graph Reasoning based on Multi-Agent Large Language Model System5 Feb 2025
- 2024Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models21 Nov 2024
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