Humans manipulate various kinds of fluids in their everyday life: creating latte art, scooping floating objects from water, rolling an ice cream cone, etc. Using robots to augment or replace human labors in these daily settings remain as a challenging task due to the multifaceted complexities of fluids. Previous research in robotic fluid manipulation mostly consider fluids governed by an ideal, Newtonian model in simple task settings (e.g., pouring). However, the vast majority of real-world fluid systems manifest their complexities in terms of the fluid's complex material behaviors and multi-component interactions, both of which were well beyond the scope of the current literature. To evaluate robot learning algorithms on understanding and interacting with such complex fluid systems, a comprehensive virtual platform with versatile simulation capabilities and well-established tasks is needed. In this work, we introduce FluidLab, a simulation environment with a diverse set of manipulation tasks involving complex fluid dynamics. These tasks address interactions between solid and fluid as well as among multiple fluids. At the heart of our platform is a fully differentiable physics simulator, FluidEngine, providing GPU-accelerated simulations and gradient calculations for various material types and their couplings. We identify several challenges for fluid manipulation learning by evaluating a set of reinforcement learning and trajectory optimization methods on our platform. To address these challenges, we propose several domain-specific optimization schemes coupled with differentiable physics, which are empirically shown to be effective in tackling optimization problems featured by fluid system's non-convex and non-smooth properties. Furthermore, we demonstrate reasonable sim-to-real transfer by deploying optimized trajectories in real-world settings.

翻译：摘要：人类在日常生活中操作各种流体：制作拿铁艺术、从水中舀取漂浮物、卷冰淇淋蛋筒等。由于流体的多重复杂性，使用机器人来增强或替代这些日常场景中的人类劳动仍是一项具有挑战性的任务。此前的机器人流体操作研究主要考虑理想牛顿流体模型下的简单任务设置（例如倾倒操作）。然而，绝大多数现实流体系统因其复杂的材料行为和多组分相互作用而展现出复杂性，这两点均远超当前文献的研究范畴。为了评估机器人学习算法在理解并交互此类复杂流体系统方面的能力，需要构建一个具备通用仿真能力和完善任务集的综合性虚拟平台。本研究提出FluidLab——一个配备多样化操作任务的仿真环境，这些任务涉及复杂流体动力学，涵盖固-液相互作用及多流体相互作用。该平台的核心是FluidEngine——一个完全可微的物理引擎，为多种材料类型及其耦合提供GPU加速的仿真与梯度计算功能。通过在该平台上评估强化学习与轨迹优化方法，我们揭示了流体操作学习中的若干挑战。为应对这些挑战，我们提出了结合可微物理的领域专用优化方案，实验证明该方案能有效处理流体系统非凸非光滑特性带来的优化问题。此外，我们通过在实际场景中部署优化轨迹，验证了从仿真到现实迁移的合理性。