让机器人迭代过程可追踪，数据和决策全程留痕。
RoboLineage: Agent-Native Data Lifecycle Governance Across Robot Policy Iterations
- 将机器人训练中的数据收集、评估、重训等环节转化为可追踪的实体对象。
- 在真实机器人操作中提升迭代效率，性能不下降且过程可审计。
- 适合需要可靠迭代记录的机器人研发团队使用。
我们提出RoboLineage，一种面向机器人策略迭代的原生数据生命周期治理系统。现代机器人策略通过反复的数据采集、审查、重训练、评估与发布决策不断优化，但这些步骤之间的证据常散落在本地工具、脚本和专家记忆中。RoboLineage通过将轨迹、评审、数据集决策、训练运行、策略元数据、评估结果、部署建议及下一阶段采集计划等表示为具有类型的谱系实体，使整个生命周期显式化。智能体可解释具身轨迹证据，将已接受数据适配至现有训练流程，维护数据健康，并在明确的实体边界下总结跨迭代状态。在真实机器人操作任务中，RoboLineage显著加快常规策略迭代速度，增强可审计性，同时保持下游策略性能。项目已开源，作为轻量级生命周期层，支持多种机器人本体和训练范式。项目页：https://robolineage.github.io/
原文摘要 · Abstract (English)
We present RoboLineage, an agent-native data lifecycle governance system for robot policy iteration. Modern robot policies improve through repeated data collection, review, retraining, evaluation, and release decisions, but the evidence connecting these steps is often scattered across local tools, scripts, and expert memory. RoboLineage makes this lifecycle explicit by representing rollouts, reviews, dataset decisions, training runs, policy metadata, evaluations, deployment recommendations, and next-collection plans as typed lineage artifacts. Agents interpret embodied rollout evidence, adapt accepted data to existing training stacks, maintain data health, and summarize cross-iteration state under explicit artifact boundaries. In real-robot manipulation workflows, RoboLineage makes routine policy iteration faster and more auditable while maintaining downstream policy performance. We open source RoboLineage as a lightweight lifecycle layer for different robot embodiments and training families. Project page: https://robolineage.github.io/
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