Recent advances in multimodal large language models (MLLMs) have opened new opportunities for embodied intelligence, enabling multimodal understanding, reasoning, and interaction, as well as continuous spatial decision-making. Nevertheless, current MLLM-based embodied systems face two critical limitations. First, Geometric Adaptability Gap: models trained solely on 2D inputs or with hard-coded 3D geometry injection suffer from either insufficient spatial information or restricted 2D generalization, leading to poor adaptability across tasks with diverse spatial demands. Second, Embodiment Constraint Gap: prior work often neglects the physical constraints and capacities of real robots, resulting in task plans that are theoretically valid but practically infeasible. To address these gaps, we introduce OmniEVA -- an embodied versatile planner that enables advanced embodied reasoning and task planning through two pivotal innovations: (1) a Task-Adaptive 3D Grounding mechanism, which introduces a gated router to perform explicit selective regulation of 3D fusion based on contextual requirements, enabling context-aware 3D grounding for diverse embodied tasks. (2) an Embodiment-Aware Reasoning framework that jointly incorporates task goals and embodiment constraints into the reasoning loop, resulting in planning decisions that are both goal-directed and executable. Extensive experimental results demonstrate that OmniEVA not only achieves state-of-the-art general embodied reasoning performance, but also exhibits a strong ability across a wide range of downstream scenarios. Evaluations of a suite of proposed embodied benchmarks, including both primitive and composite tasks, confirm its robust and versatile planning capabilities. Project page: https://omnieva.github.io

翻译：近年来，多模态大语言模型（MLLMs）的进展为具身智能开辟了新的机遇，使其能够进行多模态理解、推理与交互，以及连续的空间决策。然而，当前基于MLLM的具身系统面临两个关键局限。首先，几何适应性鸿沟：仅基于二维输入训练或采用硬编码三维几何注入的模型，要么空间信息不足，要么二维泛化能力受限，导致其在空间需求各异的任务中适应性差。其次，具身约束鸿沟：先前工作常忽略真实机器人的物理约束与能力，导致生成的任务计划理论上有效但实际不可行。为应对这些鸿沟，我们提出了OmniEVA——一种通用具身规划器，通过两项关键创新实现先进的具身推理与任务规划：（1）任务自适应三维基础机制，引入门控路由器，根据上下文需求对三维融合进行显式的选择性调控，从而为多样化的具身任务实现上下文感知的三维基础。（2）具身感知推理框架，将任务目标与具身约束共同纳入推理循环，从而生成既目标导向又可执行的规划决策。大量实验结果表明，OmniEVA不仅在通用具身推理性能上达到最先进水平，还在广泛的下游场景中展现出强大能力。对一系列提出的具身基准（包括基础任务与复合任务）的评估，证实了其稳健且通用的规划能力。项目页面：https://omnieva.github.io