机器人多相交互：利用世界模型操控耦合的液体与固体动力学
Robotic Multiphase Interaction: Manipulating Coupled Liquid and Solid Dynamics with a World Model
- National University of Singapore（新加坡国立大学）
- University of Surrey（萨里大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出机器人多相交互设定，用世界模型预测海绵等含液多孔物体的耦合液固状态，显著降低保留水预测误差并优化动作序列。
AI中文摘要：
本工作提出了机器人多相交互（RMI），在该设定中，液体进入多孔材料并与其变形的固体骨架发生机械相互作用。因此，操控可以改变孔隙体积，排出或重新分配保留的液体，同时改变抓取稳定性。溢出的液体也可能在家庭和制造环境中造成安全风险。这与大多数固体物体的操控以及涉及液体和固体但保持各相空间分离的任务不同。我们以充满水的海绵作为第一个RMI实例进行研究。我们使用隐式不可压缩多孔流与平滑粒子流体动力学作为动力学引擎，并通过添加库仑接触记忆、混合速度与力调节以及用于提升的稳定性门控来实现机器人操控。由此产生的环境将机器人命令与耦合的液体和固体状态变化联系起来。一个以动作条件化的世界模型预测在候选命令下该状态如何演变，而时间UNet使用扩散策略或整流流匹配生成动作。与基线相比，我们的世界模型将保留水预测误差降低了超过60%。世界模型从策略提案中选出的最佳动作序列进一步将预测的终端水误差减少了约一半。这些改进表明，对耦合的液体和固体状态进行建模有助于机器人预测其动作如何影响多孔物体及其内部保留的液体。
英文摘要：
This work presents \textit{Robotic Multiphase Interaction (RMI)}, a setting in which liquid enters a porous material and interacts mechanically with its deforming solid skeleton. Manipulation can therefore change pore volume, expel or redistribute retained liquid, and alter grasp stability at the same time. Spilled liquid can also create safety risks in domestic and manufacturing settings. This differs from most manipulation of solid objects and from tasks that involve both liquid and solid while keeping the phases spatially separate. We study a sponge filled with water as the first RMI example. We use implicit incompressible porous flow with smoothed particle hydrodynamics as the dynamics engine and enable robotic manipulation by adding Coulomb contact memory, hybrid velocity and force regulation, and a stability gate for lifting. The resulting environment connects robot commands to changes in the coupled liquid and solid state. A world model conditioned on actions predicts how this state evolves under candidate commands, while a temporal UNet generates actions using either Diffusion Policy or rectified flow matching. Our world model reduces retained water prediction error by more than $60\%$ compared with the baseline. The best action sequence selected by the world model from policy proposals further reduces the predicted terminal water error by about half. These improvements show that modelling the coupled liquid and solid state helps the robot predict how its actions affect both the porous object and the liquid held inside.
