FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation

• URL: http://arxiv.org/abs/2303.02346v1
• Date: Sat, 4 Mar 2023 07:24:22 GMT
• Title: FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation
• Authors: Zhou Xian, Bo Zhu, Zhenjia Xu, Hsiao-Yu Tung, Antonio Torralba, Katerina Fragkiadaki, Chuang Gan
• Abstract summary: We introduce FluidLab, a simulation environment with a diverse set of manipulation tasks involving complex fluid dynamics. At the heart of our platform is a fully differentiable physics simulator, providing GPU-accelerated simulations and gradient calculations. We propose several domain-specific optimization schemes coupled with differentiable physics.
• Score: 80.63838153351804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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