RegenHarness：一种具有证据门控递归自我改进的机器人智能体框架

中文摘要 AI 辅助

长期机器人执行需要明确区分模型的提议、控制器的终止以及验证的任务完成。我们提出了RegenHarness，一种证据门控的机器人智能体框架，将任务规划连接到异构机器人技能。其执行架构将用于上下文条件提议的模型循环与用于调度、观察、验证、承诺和有限恢复的智能体循环耦合。四个角色隔离的上下文分别处理规划、监督、验证和恢复输入。版本化内存区分观察事实与已接受的任务进展，而身份和版本绑定的提交门控制对可信任务状态的更新。运行时在显式后端契约下结合重复调度控制、资源租约和恢复预算，并在报告完成前检查原始用户目标。据我们所知，我们是首个为具身机器人智能体引入证据门控递归自我改进（RSI）协议的研究。跨任务中，执行记录激励对上下文规则、任务模板、路由和恢复策略的候选更改；固定回归检查和发布授权管理其接受；版本化部署和回滚保持配置可追溯性。该RSI协议在不进行在线模型权重更新或允许削弱提交门的情况下修订框架配置。一个真实的四足机器人部署展示了通过链接的音频、图像、轨迹和收据实现的语音触发的仓库导航、全景检查、视觉分析、消息传递、返回和语音报告。另一个电路演示了为什么完成依赖于执行历史而非仅端点接近度。综合来看，这些案例展示了在真实世界机器人任务中的集成感知、物理执行、通信和历史依赖完成。

英文摘要

Long-horizon robot execution requires a clear distinction between a model's proposal, a controller's termination, and verified task completion. We present RegenHarness, an evidence-gated robot-agent harness connecting task planning to heterogeneous robot skills. Its execution architecture couples a model loop for context-conditioned proposals with an agent loop for dispatch, observation, verification, commitment, and bounded recovery. Four role-isolated contexts separate planning, supervision, verification, and recovery inputs. Versioned memory distinguishes observed facts from accepted task progress, while an identity- and version-bound commit gate controls updates to trusted task state. The runtime combines duplicate-dispatch control, resource leases, and recovery budgets under explicit backend contracts, and checks the original user goal before reporting completion. To our knowledge, we are the first to introduce an evidence-gated recursive self-improvement (RSI) protocol for embodied robotic agents. Across missions, execution records motivate candidate changes to context rules, task templates, routing, and recovery policies; fixed regression checks and release authorization govern their acceptance; versioned rollout and rollback preserve configuration traceability. This RSI protocol revises the harness configuration without online model-weight updates or permission to weaken the commit gate. A real quadruped deployment documents voice-triggered warehouse navigation, panoramic inspection, visual analysis, message delivery, return, and spoken reporting through linked audio, images, trajectories, and receipts. A separate circuit demonstrates why completion depends on execution history rather than endpoint proximity alone. Together, the cases demonstrate integrated perception, physical execution, communication, and history-dependent completion in real-world robot tasks.

发表机构

• AI Lab, Country Garden Services Group（碧桂园服务集团AI实验室）
• School of Mechanical Science and Engineering, Huazhong University of Science and Technology（华中科技大学机械科学与工程学院）
• Omni AI

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