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

TL;DR

This paper introduces AffordBot, a framework that combines multimodal large language models with a chain-of-thought reasoning process to enable fine-grained, instruction-driven understanding of 3D scenes for embodied agents.

Contribution

It proposes a novel task of fine-grained 3D embodied reasoning and develops AffordBot, integrating MLLMs with scene rendering and active perception for improved affordance understanding.

Findings

Achieves state-of-the-art results on SceneFun3D dataset

Demonstrates strong generalization with only 3D point cloud input

Shows effective physically grounded reasoning in 3D scenes

Abstract

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.