机器人有了自己的操作系统：能记一辈子，还能自己进化
机器人通常只能执行单一指令，遇到复杂任务就容易断片。这篇论文给机器人装了一个「操作系统」ABot-AgentOS，它像人的大脑一样，能规划步骤、调用工具、检查结果，最关键的是拥有一个「终身多模态记忆」——把看到的东西、听到的对话、走过的路线都存成一张知识图谱，下次遇到类似场景可以直接调用。而且它还会自己复盘：任务失败后自动分析原因，把修复方案存起来，下次遇到同样问题就能避开。在200多个任务测试中，成功率比普通控制器高出一截。虽然离家用机器人还很远，但它指明了方向：未来的机器人不是更快的执行器，而是会学习、会记忆的智能体。
📄 原文摘要(英文)
Recent VLM and VLA systems have improved robotic perception and action prediction, yet long-horizon embodied agents still require a general runtime layer for reasoning, memory, tool use, verification, and cross-embodiment execution. We present ABot-AgentOS, a general robotic Agent Operating System that sits above low-level controllers and provides a deliberative agent layer for scene-conditioned planning, context-isolated skill execution, multi-stage verification, multi-modal memory, and edge-cloud collaboration. To evaluate such systems, we introduce EmbodiedWorldBench, an executable benchmark with 16 indoor, outdoor, and hybrid scenes, four difficulty levels, and over 200 tasks involving navigation, object search, NPC dialogue, dynamic events, and trace-grounded scoring. ABot-AgentOS further introduces Universal Multi-modal Graph Memory, a persistent source-grounded substrate that converts dialogue, visual observations, spatial context, temporal relations, and task traces into typed nodes and edges. A failure-driven self-evolution loop converts diagnosed memory failures into gated runtime evo-assets that are promoted only to later evaluation splits, preventing current-split ground-truth leakage while enabling continual improvement. On an initial EmbodiedWorldBench subset, ABot-AgentOS improves over a single-controller baseline in both task success and goal completion. Across memory benchmarks, ABot-AgentOS Static achieves 87.5 on LoCoMo, 59.9 on OpenEQA EM-EQA, 88.6 on Mem-Gallery, and 76.5 Acc@All on NExT-QA; self-evolution further improves LoCoMo to 88.7, OpenEQA to 60.4, and Mem-Gallery to 89.0. These results suggest that a general Agent OS layer can improve long-horizon embodied execution while providing persistent, auditable memory for continual interaction.