RoboLayout: 用于具身智能体的可微3D场景生成
RoboLayout: Differentiable 3D Scene Generation for Embodied Agents
AI总结：
RoboLayout通过引入智能体感知推理和改进优化稳定性，提升3D场景生成在具身智能体交互中的可行性与物理合理性。
摘要：
近年来，视觉语言模型（VLMs）在空间推理和从开放式语言指令生成3D场景布局方面展现出强大潜力。然而，生成不仅语义连贯且能被具身智能体交互的布局仍具挑战性，尤其是在物理受限的室内环境中。本文介绍了RoboLayout，作为LayoutVLM的扩展，通过引入智能体感知推理和改进的优化稳定性来增强原始框架。RoboLayout将显式可达性约束整合到可微布局优化过程中，使生成的布局能够被具身智能体导航和操作。重要的是，智能体抽象不限于特定机器人平台，可以代表具有不同物理能力的多样化实体，如服务机器人、仓储机器人、不同年龄段的人类或动物，允许环境设计根据预期智能体进行定制。此外，提出了一种局部细化阶段，可选择性地重新优化有问题的物体位置，同时保持场景其余部分不变，提高收敛效率而不增加全局优化迭代次数。总体而言，RoboLayout在保持LayoutVLM强大的语义对齐和物理合理性的同时，增强了适用于以智能体为中心的室内场景生成的适用性，通过在多种场景配置上的实验结果得到了验证。
英文摘要：
Recent advances in vision language models (VLMs) have shown strong potential for spatial reasoning and 3D scene layout generation from open-ended language instructions. However, generating layouts that are not only semantically coherent but also feasible for interaction by embodied agents remains challenging, particularly in physically constrained indoor environments. In this paper, RoboLayout is introduced as an extension of LayoutVLM that augments the original framework with agent-aware reasoning and improved optimization stability. RoboLayout integrates explicit reachability constraints into a differentiable layout optimization process, enabling the generation of layouts that are navigable and actionable by embodied agents. Importantly, the agent abstraction is not limited to a specific robot platform and can represent diverse entities with distinct physical capabilities, such as service robots, warehouse robots, humans of different age groups, or animals, allowing environment design to be tailored to the intended agent. In addition, a local refinement stage is proposed that selectively reoptimizes problematic object placements while keeping the remainder of the scene fixed, improving convergence efficiency without increasing global optimization iterations. Overall, RoboLayout preserves the strong semantic alignment and physical plausibility of LayoutVLM while enhancing applicability to agent-centric indoor scene generation, as demonstrated by experimental results across diverse scene configurations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
