[
    {
        "id": "osp-25492",
        "type": "article-journal",
        "title": "ECoMEM: Explicit Concept Memory for Memory-Dependent Robot Control",
        "author": [
            {
                "family": "Liu",
                "given": "Yize"
            },
            {
                "family": "Wang",
                "given": "Ke"
            },
            {
                "family": "Schwager",
                "given": "Mac"
            },
            {
                "family": "Xu",
                "given": "Yiqing"
            },
            {
                "family": "Wu",
                "given": "Jiajun"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/ecomem-explicit-concept-memory-for-memory-dependent-robot-control",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "A robot may lose sight of an object it must later retrieve, need to recall what a person demonstrated earlier, or track which steps of a task it has already completed. Current vision-language-action (VLA) policies often fail once the information needed for action disappears from the current observation, making memory critical for long-horizon robot behavior. Existing approaches typically provide longer histories or learn implicit memory from observation-action trajectories. But action supervision tells a policy how to act, not what to remember: it does not specify which past facts should persist or how they should change as new evidence arrives. We therefore separate maintaining an evidence-grounded account of the past from learning how to act on it. This insight motivates Explicit Concept Memory (ECoMEM), which represents task-relevant history with a reusable library of grounded concepts. An evidence-based Writer selects and updates these records, while a learned Reader turns them into memory tokens that directly condition the VLA. Across 16 RoboMME tasks, ECoMEM leads the evaluated robot policies on 15 tasks. On two new real-robot tasks, the same memory library either transfers directly or requires only one new concept, achieving 86.1% success versus 8.6% for a no-memory VLA. These results show that explicit concepts provide a reusable and extensible memory interface for robot control. Project website: https://ecomem.github.io/"
    }
]