Governed Capability Evolution for Embodied Agents: Safe Upgrade, Compatibility Checking, and Runtime Rollback for Embodied Capability Modules
2026-04-09 • Robotics
RoboticsArtificial Intelligence
AI summaryⓘ
The authors studied how robots or embodied agents can safely update their skills without causing problems during use. They created a step-by-step process that checks new skill versions for compatibility with rules, behavior, and safety before fully using them. Their method includes testing in safe environments, monitoring real use, and rolling back if issues appear. They found this approach keeps the agents working well while preventing unsafe activations, unlike simple upgrades that often cause problems.
embodied agentscapability modulessoftware upgraderuntime governancesandbox evaluationshadow deploymentrollbackpolicy constraintsbehavioral compatibilitytask success
Authors
Xue Qin, Simin Luan, John See, Cong Yang, Zhijun Li
Abstract
Embodied agents are increasingly expected to improve over time by updating their executable capabilities rather than rewriting the agent itself. Prior work has separately studied modular capability packaging, capability evolution, and runtime governance. However, a key systems problem remains underexplored: once an embodied capability module evolves into a new version, how can the hosting system deploy it safely without breaking policy constraints, execution assumptions, or recovery guarantees? We formulate governed capability evolution as a first-class systems problem for embodied agents. We propose a lifecycle-aware upgrade framework in which every new capability version is treated as a governed deployment candidate rather than an immediately executable replacement. The framework introduces four upgrade compatibility checks -- interface, policy, behavioral, and recovery -- and organizes them into a staged runtime pipeline comprising candidate validation, sandbox evaluation, shadow deployment, gated activation, online monitoring, and rollback. We evaluate over 6 rounds of capability upgrade with 15 random seeds. Naive upgrade achieves 72.9% task success but drives unsafe activation to 60% by the final round; governed upgrade retains comparable success (67.4%) while maintaining zero unsafe activations across all rounds (Wilcoxon p=0.003). Shadow deployment reveals 40% of regressions invisible to sandbox evaluation alone, and rollback succeeds in 79.8% of post-activation drift scenarios.