arXiv 2508.156632025-08-21cs.ROcs.AI

心智与运动对齐：面向移动操作中任务规划与底层策略的 IsaacSim 联合评估基准

Mind and Motion Aligned: A Joint Evaluation IsaacSim Benchmark for Task Planning and Low-Level Policies in Mobile Manipulation

AIRI；Moscow Institute of Physics and Technology (MIPT)（莫斯科物理技术学院）；Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）；Sberbank, Robotics Center（Sberbank机器人中心）；Skoltech

Nikita Kachaev, Andrei Spiridonov, Andrey Gorodetsky, Kirill Muravyev, Nikita Oskolkov, Aditya Narendra, Vlad Shakhuro, Dmitry Makarov, Aleksandr I. Panov, Poli… 展开作者

Nikita Kachaev, Andrei Spiridonov, Andrey Gorodetsky, Kirill Muravyev, Nikita Oskolkov, Aditya Narendra, Vlad Shakhuro, Dmitry Makarov, Aleksandr I. Panov, Polina Fedotova, Alexey K. Kovalev

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AI总结：

本文提出 Kitchen-R 基准，在 Isaac Sim 数字孪生厨房环境中统一评估任务规划与底层控制，并提供视觉语言模型规划、扩散策略控制及轨迹采集系统，支持独立与集成评估，弥补具身 AI 中语言引导机器人整体评测的空白。

摘要：

基准对于评估机器人学和具身人工智能的进展至关重要。然而，面向高层语言指令跟随的基准通常假设底层执行是完美的，而面向底层机器人控制的基准则依赖简单的一步式命令，二者之间存在显著差距。这种脱节使得无法对任务规划和物理执行都至关重要的集成系统进行全面评估。为解决这一问题，我们提出了 Kitchen-R，这是一个在模拟厨房环境中统一任务规划与底层控制评估的新型基准。Kitchen-R 基于 Isaac Sim 模拟器构建为数字孪生，包含超过 500 条复杂语言指令，并支持移动操作机器人。我们为该基准提供了基线方法，包括基于视觉语言模型的任务规划策略和基于扩散策略的底层控制策略。我们还提供了一个轨迹采集系统。我们的基准为三种评估模式提供了灵活框架：规划模块的独立评估、控制策略的独立评估，以及至关重要的整个系统的集成评估。Kitchen-R 弥合了具身人工智能研究中的一个关键差距，使语言引导的机器人智能体能够获得更全面、更真实的基准测试。

英文摘要：

Benchmarks are crucial for evaluating progress in robotics and embodied AI. However, a significant gap exists between benchmarks designed for high-level language instruction following, which often assume perfect low-level execution, and those for low-level robot control, which rely on simple, one-step commands. This disconnect prevents a comprehensive evaluation of integrated systems where both task planning and physical execution are critical. To address this, we propose Kitchen-R, a novel benchmark that unifies the evaluation of task planning and low-level control within a simulated kitchen environment. Built as a digital twin using the Isaac Sim simulator and featuring more than 500 complex language instructions, Kitchen-R supports a mobile manipulator robot. We provide baseline methods for our benchmark, including a task-planning strategy based on a vision-language model and a low-level control policy based on diffusion policy. We also provide a trajectory collection system. Our benchmark offers a flexible framework for three evaluation modes: independent assessment of the planning module, independent assessment of the control policy, and, crucially, an integrated evaluation of the whole system. Kitchen-R bridges a key gap in embodied AI research, enabling more holistic and realistic benchmarking of language-guided robotic agents.

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。