外观
论文arXiv 2610.2309812 天前
InstAgent：一种用于异构机器人协作的递归自我改进 Agent 操作系统
原文链接：InstAgent: A Recursively Self-Improving Agent Operating System for Heterogeneous Robot Collaboration（arXiv 2610.23098 · 发布日期：2026-10-16）
InstAgent: A Recursively Self-Improving Agent Operating System for Heterogeneous Robot Collaboration
针对异构机器人协作中的经验复用、上下文记忆与依赖建模难题，InstAgent以行为树和共享多模态时空记忆组织协作，并在真实多机器人任务上验证。
核心要点
- 异构机器人团队可结合多种能力执行复杂长程任务，但现有系统难以保留纠错经验、维持交接上下文。
- InstAgent用agentic行为树显式建模能力分配、机器人间依赖与交接检查，并通过RSI从反馈中更新协作程序。
- 在20个真实世界多机器人任务和三个高层模型上，任务成功率、首次完成率与执行效率均持续提升。
验证范围与局限：验证范围为20个真实世界多机器人任务和三个高层模型。
英文摘要 (Abstract)
Heterogeneous robot teams enable complex, long-horizon tasks by combining complementary sensing, mobility, and manipulation capabilities across multiple embodiments. Yet existing systems struggle to retain execution-time corrections as reusable experience, maintain task-relevant context memory across space and time for reliable handoffs, and explicitly represent the inter-robot dependencies required for structured collaboration. In order to address these challenges, we present InstAgent, a recursively self-improving (RSI) Agent Operating System for heterogeneous robot collaboration. InstAgent organizes team behaviors as agentic behavior trees (BTs) that explicitly model capability assignment, inter-robot dependencies and handoff checks. The RSI mechanism updates these collaboration programs from execution feedback and preserves successful revisions for reuse in future tasks, while shared multimodal spatio-temporal memory provides context for planning and handoff validation. Experiments on 20 real-world multi-robot tasks with three high-level models show consistent gains in task success, first-attempt completion, and execution efficiency.
作者：Haoyang Luo, Junhao Qin, Jingyan Cao, Yi Huang, Guangjie Yang, Xiaoyu Chen, Haotao Lu, Tianxiang Gui, Ye Shi, Jingya Wang
来源
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