构建长时序家务任务基准，评估机器人长期规划能力。
When Robots Do the Chores: A Benchmark and Agent for Long-Horizon Household Task Execution
- 用自由指令定义任务，聚焦高层认知而非底层控制。
- 提出HoloMind模型，实现任务依赖管理与持续记忆。
- 验证大模型仍难完成复杂家务，凸显长期规划挑战。
长时序家务任务需要强大的高层规划与持续推理能力，但现有具身AI基准多关注短时序导航或操作，且任务类别固定。我们提出LongAct基准，用于评估通过自由形式指令定义的长时序家务任务中的规划自主性。通过抽象化具身相关的低层控制，LongAct专注于高层认知能力，如指令理解、依赖关系管理、记忆保持与自适应规划。我们进一步提出HoloMind，一个由视觉语言模型驱动的智能体，包含基于有向无环图（DAG）的长时序分层规划器、多模态空间记忆以实现持久世界建模、情景记忆用于经验复用，以及全局评判器进行反思式监督。使用GPT-5和Qwen3-VL模型的实验表明，HoloMind显著提升长时序任务表现，同时减少对模型规模的依赖。即使顶尖模型在目标完成率上仅达59%，全任务成功率仅为16%，凸显LongAct的难度及具身智能体在长期规划上的迫切需求。
原文摘要 · Abstract (English)
Long-horizon household tasks demand robust high-level planning and sustained reasoning capabilities, which are largely overlooked by existing embodied AI benchmarks that emphasize short-horizon navigation or manipulation and rely on fixed task categories. We introduce LongAct, a benchmark designed to evaluate planning-level autonomy in long-horizon household tasks specified through free-form instructions. By abstracting away embodiment-specific low-level control, LongAct isolates high-level cognitive capabilities such as instruction understanding, dependency management, memory maintenance, and adaptive planning. We further propose HoloMind, a VLM-driven agent with a DAG-based long-horizon hierarchical planner, a Multimodal Spatial Memory for persistent world modeling, an Episodic Memory for experience reuse, and a global Critic for reflective supervision. Experiments with GPT-5 and Qwen3-VL models show that HoloMind substantially improves long-horizon performance while reducing reliance on model scale. Even top models achieve only 59% goal completion and 16% full-task success, underscoring the difficulty of LongAct and the need for stronger long-horizon planning in embodied agents.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。