Feedback Motion Planning for Liquid Transfer using Supervised Learning
This addresses the challenge of real-time liquid manipulation in robotics, though it appears incremental as it combines existing techniques like system identification and neural networks for a specific application.
The paper tackles the problem of motion planning for liquid transfer between containers by developing a receding-horizon optimization algorithm that incorporates fluid constraints and collision avoidance, achieving high success rates in simulated 2D and 3D benchmarks.
We present a novel motion planning algorithm for transferring a liquid body from a source to a target container. Our approach uses a receding-horizon optimization strategy that takes into account fluid constraints and avoids collisions. In order to efficiently handle the high-dimensional configuration space of a liquid body, we use system identification to learn its dynamics characteristics using a neural network. We generate the training dataset using stochastic optimization in a transfer-problem-specific search space. The runtime feedback motion planner is used for real-time planning and we observe high success rate in our simulated 2D and 3D fluid transfer benchmarks.