Visual Closed-Loop Control for Pouring Liquids
This addresses a challenging task in robotics for applications like manufacturing or domestic assistance, and is incremental as it builds on existing control methods with new visual estimation techniques.
The paper tackled the problem of robots pouring specific amounts of liquid using visual feedback, achieving an average deviation of 38ml from the target amount.
Pouring a specific amount of liquid is a challenging task. In this paper we develop methods for robots to use visual feedback to perform closed-loop control for pouring liquids. We propose both a model-based and a model-free method utilizing deep learning for estimating the volume of liquid in a container. Our results show that the model-free method is better able to estimate the volume. We combine this with a simple PID controller to pour specific amounts of liquid, and show that the robot is able to achieve an average 38ml deviation from the target amount. To our knowledge, this is the first use of raw visual feedback to pour liquids in robotics.