Reasoning About Liquids via Closed-Loop Simulation
This addresses the challenge of accurate liquid simulation for robotics applications, though it appears incremental as it builds on existing simulation and perception methods.
The paper tackles the problem of simulation divergence from reality in liquid interactions by introducing closed-loop simulation that uses real-time perception to correct errors, resulting in effective prevention of large divergence and enabling reasoning about occluded liquids.
Simulators are powerful tools for reasoning about a robot's interactions with its environment. However, when simulations diverge from reality, that reasoning becomes less useful. In this paper, we show how to close the loop between liquid simulation and real-time perception. We use observations of liquids to correct errors when tracking the liquid's state in a simulator. Our results show that closed-loop simulation is an effective way to prevent large divergence between the simulated and real liquid states. As a direct consequence of this, our method can enable reasoning about liquids that would otherwise be infeasible due to large divergences, such as reasoning about occluded liquid.