SOURCE-LINKED INTELLIGENCE

AGM: Achievement-Grounded Memory for Closed-Loop Agents with Frozen VLA Policies

arXiv · AI, language, vision and robotics · article · Aug 30, 2026 · UTC

Frozen vision-language-action (VLA) policies offer broad manipulation skills but execute open-loop action chunks without tracking task progress, so the agent cannot reliably decide whether to continue, retry, or terminate. External memory is a natural remedy, yet it can be harmful when attempted actions are treated as completed progress, turning local execution errors into persistent task-state errors. We propose Achievement-Grounded Memory (AGM), a lightweight closed-loop framework for frozen VLA policies that represents a task as a subgoal sequence with a progress pointer and advances this m

Read original source ↗ Open in workspace

recordType

paper

region

Global