发表机构
Imprintx Robotics; Electric Power Research Institute of State Grid Jibei Electric Power Co., Ltd.; North China Electric Power Research Institute Co., Ltd.; Institute of Automation, Chinese Academy of Sciences（印迹机器人; 国网冀北电力有限公司电力科学研究院; 华北电力科学研究院有限责任公司; 中国科学院自动化研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
DeltaWorld通过Delta-LTM预测动作引起的潜在增量并添加至当前状态，结合交互感知对齐，实现物理一致的机器人操作世界模拟，显著降低FVD和LPIPS。
AI 中文摘要
交互式世界模拟器可以通过预测动作后果，同时减少对重复物理展开的依赖，为机器人规划、策略训练和评估提供可扩展的环境。为了服务于这些应用，它们必须生成能够忠实响应机器人动作并保持机器人-物体交互动力学在长时间跨度内一致性的未来图像序列。然而，现有的世界模型通常预测整个下一个潜在状态，往往无法捕捉机器人动作引起的细微变化。这种遗漏可能导致物理上不合理的后果，包括物体相互穿透和过度变形。为了解决这一局限性，我们提出了DeltaWorld，一种用于机器人操作的物理一致的交互式世界模拟器。我们的方法引入了Delta潜在转移模型（Delta-LTM），该模型预测动作引起的潜在特征变化，并将其添加到当前潜在状态以获得下一个状态，而不是直接预测下一个潜在状态。为了减轻预测未来帧中的物体相互穿透和过度变形，引入了交互感知潜在对齐，以构建反事实交互区域并监督与交互相关的潜在变化。DeltaWorld在IWS操作基准和一个涵盖多种机器人形态和操作任务的自收集跨机器人数据集上进行了评估。在跨机器人数据集上，与IWS基线相比，DeltaWorld将FVD降低了46.6%，LPIPS降低了31.1%。这些结果突显了DeltaWorld在机器人操作中长期动作条件视频预测方面的潜力。
英文摘要
Interactive world simulators can provide scalable environments for robot planning, policy training, and evaluation by predicting action consequences while reducing reliance on repeated physical rollouts. To serve these applications, they must generate future image sequences that respond faithfully to robot actions and preserve the dynamics of robot-object interactions over long horizons. However, existing world models typically predict the entire next latent state and often fail to capture subtle changes induced by robot actions. Such omissions can produce physically implausible outcomes, including object interpenetration and excessive deformation. To address this limitation, we propose DeltaWorld, a physically consistent interactive world simulator for robotic manipulation. Our method introduces the Delta Latent Transition Model (Delta-LTM), which predicts action-induced latent feature changes and adds them to the current latent state to obtain the next state, rather than predicting the next latent state directly. To mitigate object interpenetration and excessive deformation in predicted future frames, Interaction-aware Latent Alignment is introduced to construct counterfactual interaction regions and supervise interaction-related latent changes. DeltaWorld is evaluated on the IWS manipulation benchmark and a self-collected cross-robot dataset covering multiple robot embodiments and manipulation tasks. On the cross-robot dataset, DeltaWorld reduces FVD by 46.6% and LPIPS by 31.1% relative to the IWS baseline. These results highlight the potential of DeltaWorld for long-horizon action-conditioned video prediction in robotic manipulation.
