AffordBot: 3D Fine-grained Embodied Reasoning via Multimodal Large Language Models

Zhen Li (Shenzhen Future Network of Intelligence Institute and Guangdong Provincial Key Laboratory of Future Networks of Intelligence, The Chinese University of Hong Kong (Shenzhen)) · Na Zhao (Singapore University of Technology and Design) · Xun Yang (University of Science and Technology of China) · Yuchen Wu (Singapore University of Technology and Design) · Xinyi Wang (University of Science and Technology of China) · Yanlong Xu

3d element candidatesactionable elementsactive perceptionaffordance reasoningaffordbotchain-of-thought reasoningfine-grained 3d embodied reasoninginstruction-driven groundingmotion axismotion typemultimodal large language modelsphysically grounded reasoningscenefun3d datasetspatial locationstructured tripletsurround-view images

Effective human-agent collaboration in physical environments requires understanding not only what to act upon, but also where the actionable elements are and how to interact with them. Existing approaches often operate at the object level or disjointedly handle fine-grained affordance reasoning, lacking coherent, instruction-driven grounding and reasoning. In this work, we introduce a new task: Fine-grained 3D Embodied Reasoning, which requires an agent to predict, for each referenced affordance element in a 3D scene, a structured triplet comprising its spatial location, motion type, and motion axis, based on a task instruction. To solve this task, we propose AffordBot, a novel framework that integrates Multimodal Large Language Models (MLLMs) with a tailored chain-of-thought (CoT) reasoning paradigm. To bridge the gap between 3D input and 2D-compatible MLLMs, we render surround-view images of the scene and project 3D element candidates into these views, forming a rich visual representation aligned with the scene geometry. Our CoT pipeline begins with an active perception stage, prompting the MLLM to select the most informative viewpoint based on the instruction, before proceeding with step-by-step reasoning to localize affordance elements and infer plausible interaction motions. Evaluated on the SceneFun3D dataset, AffordBot achieves state-of-the-art performance, demonstrating strong generalization and physically grounded reasoning with only 3D point cloud input and MLLMs.