{
  "schema_version": 1,
  "slug": "maal",
  "canonical_url": "https://akira-l.github.io/publications/maal/",
  "language_urls": {
    "en": "https://akira-l.github.io/publications/maal/",
    "zh-CN": "https://akira-l.github.io/zh/publications/maal/"
  },
  "title": "MAAL: Multimodality-Aware Autoencoder-based Affordance Learning for 3D Articulated Objects",
  "short_title": "MAAL",
  "authors": [
    "Yuanzhi Liang",
    "Xiaohan Wang",
    "Linchao Zhu",
    "Yi Yang"
  ],
  "publication": {
    "kind": "conference",
    "venue": "IEEE/CVF International Conference on Computer Vision (ICCV 2023)",
    "citation_container_title": "2023 IEEE/CVF International Conference on Computer Vision (ICCV)",
    "status": "published",
    "year": 2023,
    "publication_date": "2023",
    "publisher": "IEEE",
    "pages": "217-227"
  },
  "identifiers": {
    "doi": "10.1109/ICCV51070.2023.00027"
  },
  "arxiv_dates": {},
  "keywords": [
    "3D affordance learning",
    "articulated objects",
    "multimodal learning",
    "autoencoder",
    "robotic interaction"
  ],
  "official_abstract": "Inferring affordance for 3D articulated objects is a challenging and practical problem. It is a primary problem for applying robots to real-world scenarios. The exploration can be summarized as figuring out where to act and how to act. Correspondingly, the task mainly requires producing actionability scores, action proposals, and success likelihood scores according to the given 3D object information and robotic information. Current works usually directly process multi-modal inputs with early fusion and apply critic networks to produce scores, which leads to insufficient multi-modal learning ability and inefficiently iterative training in multiple stages. This paper proposes a novel Multimodality-Aware Autoencoder-based affordance Learning (MAAL) for the 3D object affordance problem. It is an efficient pipeline, trained in one go, and only requires a few positive samples in training data. More importantly, MAAL contains a MultiModal Energized Encoder (MME) for better multi-modal learning. It comprehensively models all multi-modal inputs from 3D objects and robotic actions. Jointly considering information from multiple modalities, the encoder further learns interactions between robots and objects. MME empowers the better multi-modal learning ability for understanding object affordance. Experimental results and visualizations, based on a large-scale dataset PartNet-Mobility, show the effectiveness of MAAL in learning multi-modal data and solving the 3D articulated object affordance problem.",
  "official_abstract_rights": "Excluded from the site's CC BY 4.0 license; original paper rights apply.",
  "primary_source": {
    "label": "IEEE version of record",
    "url": "https://doi.org/10.1109/ICCV51070.2023.00027",
    "version": "ICCV 2023 version of record; CVF open-access copy cross-checked",
    "method_locator": "Abstract; MAAL and MultiModal Energized Encoder sections",
    "evidence_locator": "Abstract; PartNet-Mobility experiments and visualizations"
  },
  "source_checked": "2026-07-31",
  "verification_status": "author-verified",
  "author_verified_on": "2026-07-31",
  "commentary": {
    "license": "https://creativecommons.org/licenses/by/4.0/",
    "en": {
      "summary": "MAAL learns affordances for 3D articulated objects with a one-stage autoencoder-based pipeline and a MultiModal Energized Encoder that jointly models object geometry, robot actions, and their interactions while requiring only a small number of positive samples.",
      "problem": "Affordance prediction must infer where and how a robot can act from heterogeneous object and action information; early fusion and multi-stage critic pipelines can learn these modalities inefficiently.",
      "contributions": [
        "Recasts 3D articulated-object affordance learning as a multimodality-aware autoencoder pipeline trained in one stage.",
        "Introduces the MultiModal Energized Encoder to model object and robotic-action modalities jointly.",
        "Targets data efficiency by training with few positive interaction samples."
      ],
      "evidence": "Experiments and visualizations on PartNet-Mobility support the method's multimodal learning and affordance-prediction claims. Exact task definitions, actionability metrics, and comparisons should be cited from the ICCV paper.",
      "limitations": "The reported scope is articulated-object affordance learning under the PartNet-Mobility setup. Performance in unmodeled real-world sensing, manipulation hardware, or object categories is not established by the abstract.",
      "positioning": "MAAL is an affordance-learning method that changes both the learning pipeline and multimodal representation, replacing early-fusion, multi-stage scoring with a one-go autoencoder formulation.",
      "citation_ready": "Liang et al. propose MAAL, a one-stage autoencoder-based framework whose MultiModal Energized Encoder jointly represents 3D object and robotic-action information for articulated-object affordance learning."
    },
    "zh-CN": {
      "summary": "MAAL 用单阶段 autoencoder pipeline 学习 3D articulated object affordance，其中 MultiModal Energized Encoder 联合建模物体几何、机器人动作及其交互，并只需要少量 positive sample。",
      "problem": "Affordance prediction 需要从异构的物体与动作信息中判断机器人可以在哪里、以何种方式操作；early fusion 与多阶段 critic pipeline 对这些模态的利用可能不充分且训练低效。",
      "contributions": [
        "将 3D articulated-object affordance learning 重写为一次训练完成的 multimodality-aware autoencoder pipeline。",
        "提出 MultiModal Energized Encoder，联合建模物体与机器人动作模态。",
        "面向数据效率，只需少量成功交互样本进行训练。"
      ],
      "evidence": "PartNet-Mobility 上的实验和可视化支持其多模态学习与 affordance prediction 结论；准确任务定义、actionability 指标和比较应引用 ICCV 正式论文。",
      "limitations": "论文展示范围是 PartNet-Mobility 设置下的 articulated-object affordance learning；摘要并未证明它在未建模的真实传感、操作硬件或物体类别上同样有效。",
      "positioning": "MAAL 同时改变了 affordance learning 的训练流程和多模态表示，以一次训练的 autoencoder formulation 替代 early-fusion、多阶段打分。",
      "citation_ready": "Liang 等提出 MAAL，通过 MultiModal Energized Encoder 联合表示三维物体与机器人动作信息，并以单阶段 autoencoder 框架学习 articulated-object affordance。"
    }
  },
  "citation": {
    "id": "liang2023maal",
    "type": "paper-conference",
    "title": "MAAL: Multimodality-Aware Autoencoder-based Affordance Learning for 3D Articulated Objects",
    "author": [
      {
        "given": "Yuanzhi",
        "family": "Liang"
      },
      {
        "given": "Xiaohan",
        "family": "Wang"
      },
      {
        "given": "Linchao",
        "family": "Zhu"
      },
      {
        "given": "Yi",
        "family": "Yang"
      }
    ],
    "container-title": "2023 IEEE/CVF International Conference on Computer Vision (ICCV)",
    "issued": {
      "date-parts": [
        [
          2023
        ]
      ]
    },
    "URL": "https://doi.org/10.1109/ICCV51070.2023.00027",
    "abstract": "Inferring affordance for 3D articulated objects is a challenging and practical problem. It is a primary problem for applying robots to real-world scenarios. The exploration can be summarized as figuring out where to act and how to act. Correspondingly, the task mainly requires producing actionability scores, action proposals, and success likelihood scores according to the given 3D object information and robotic information. Current works usually directly process multi-modal inputs with early fusion and apply critic networks to produce scores, which leads to insufficient multi-modal learning ability and inefficiently iterative training in multiple stages. This paper proposes a novel Multimodality-Aware Autoencoder-based affordance Learning (MAAL) for the 3D object affordance problem. It is an efficient pipeline, trained in one go, and only requires a few positive samples in training data. More importantly, MAAL contains a MultiModal Energized Encoder (MME) for better multi-modal learning. It comprehensively models all multi-modal inputs from 3D objects and robotic actions. Jointly considering information from multiple modalities, the encoder further learns interactions between robots and objects. MME empowers the better multi-modal learning ability for understanding object affordance. Experimental results and visualizations, based on a large-scale dataset PartNet-Mobility, show the effectiveness of MAAL in learning multi-modal data and solving the 3D articulated object affordance problem.",
    "keyword": "3D affordance learning, articulated objects, multimodal learning, autoencoder, robotic interaction",
    "publisher": "IEEE",
    "DOI": "10.1109/ICCV51070.2023.00027",
    "page": "217-227"
  },
  "resources": [
    {
      "label": "Version of record",
      "url": "https://doi.org/10.1109/ICCV51070.2023.00027"
    },
    {
      "label": "Open access",
      "url": "https://openaccess.thecvf.com/content/ICCV2023/html/Liang_MAAL_Multimodality-Aware_Autoencoder-Based_Affordance_Learning_for_3D_Articulated_Objects_ICCV_2023_paper.html"
    },
    {
      "label": "PDF",
      "url": "https://openaccess.thecvf.com/content/ICCV2023/papers/Liang_MAAL_Multimodality-Aware_Autoencoder-Based_Affordance_Learning_for_3D_Articulated_Objects_ICCV_2023_paper.pdf"
    }
  ]
}