RoboLineage: Agent-Native Data Lifecycle Governance Across Robot Policy Iterations
For robotics researchers and engineers, RoboLineage addresses the problem of scattered evidence in policy iteration by providing a structured, auditable lifecycle, though the improvement is incremental as it formalizes existing practices.
RoboLineage introduces an agent-native data lifecycle governance system that structures robot policy iteration into typed lineage artifacts, enabling faster and more auditable policy updates while maintaining performance in real-robot manipulation workflows.
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/