用五个语义角色构建世界模型，让机器人规划更懂任务意图。
Beyond Instance Slots: Semantically Rich World Models for Physical Interaction Planning
- 以五类功能角色建模世界，明确物体在任务中的作用。
- 可跨场景迁移，在四套LIBERO仿真中平均提升32%成功率。
- 适合需要理解动作意义的复杂物理交互任务研究者。
用于物理交互的世界模型通常训练为预测未来观测或潜在特征；然而，面向规划的模型必须回答一个根本性问题：候选动作是否能产生与任务一致的未来并保持关键关系。单一状态表示会掩盖底层实体，而标准实例级物体槽位仅标识存在物，无法说明每个实体在任务上下文中的角色。为此，我们提出语义丰富世界模型（SR-WM），其围绕五种功能角色构建：夹持器、目标、目标点、关系和阶段。在SR-WM中，视觉实体编码器从预训练补丁特征中提取软实体假设，使分割掩码可作为可选先验，而非强制状态表示或输入。角色绑定模块随后将这些假设映射到特定任务角色，动作条件动态模型则预测角色转移及细粒度语义，包括抓取/接触、谓词建立、关系保持、固定装置状态和阶段变化。关键在于，这一统一角色状态支持下游多候选动作生成、阶段感知重排序与违规感知后缀重采样。全面评估涵盖全部四个LIBERO仿真套件、跨套件迁移、感知诊断与动作敏感性分析。该形式将对象中心预测转化为连接视觉动态与规划导向决策的语义接口。
原文摘要 · Abstract (English)
World models for physical interaction are typically trained to predict future observations or latent features; however, a planning-oriented model must answer a fundamentally different question: whether a candidate action produces a task consistent future while preserving essential relations. Monolithic state representations obscure the underlying entities, while standard instance-level object slots merely identify what is present without specifying what role each entity plays in the task context. To bridge this gap, we present the Semantically Rich World Model (SR-WM), a task-conditioned world model structured around five functional roles: gripper, target, goal, relation, and phase. Within SR-WM, a visual entity encoder extracts soft entity hypotheses from pretrained patch features, allowing segmentation masks to serve as optional proposal priors without mandating them as required state representations or inference inputs. A role binder subsequently maps these hypotheses to task-specific roles, while an action conditioned dynamics model predicts role transitions alongside fine-grained semantics, including grasp/contact, predicate establishment, relation preservation, fixture state, and phase change. Crucially, this unified role state grounds downstream multi-candidate action generation, stage-aware reranking, and violation-aware suffix resampling. Our comprehensive evaluation protocol spans all four LIBERO simulation suites, cross-suite transfer, perception diagnostics, and action sensitivity analysis. Ultimately, this formulation transforms object-centric prediction into a semantic interface linking visual dynamics with planning-oriented decision making
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