Feedback Motion Planning for Liquid Transfer using Supervised Learning

2016/09/12 by Zherong Pan, Dinesh Manocha, Pan, Zherong +1
Computer Science · Engineering · #AI-based Problem Solving and Planning #FOS: Computer and information sciences #Robotic Path Planning Algorithms #Robotics (cs.RO) #Robotics and Sensor-Based Localization

paper · pdf · doi:10.48550/arxiv.1609.03433

openalex publication_date 2016/09/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Folding Deformable Objects using Predictive Simulation and Trajectory Optimization
• A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

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