Abstract-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.

No abstract

Humans have rich understanding of liquid containers and their contents; for example, we can effortlessly pour water from a pitcher to a cup. Doing so requires estimating the volume of the cup, approximating the amount of water in the pitcher, and predicting the behavior of water when we tilt the pitcher. Very little attention in computer vision has been made to liquids and their containers. In this paper, we study liquid containers and their contents, and propose methods to estimate the volume of containers, approximate the amount of liquid in them, and perform comparative volume estimations all from a single RGB image. Furthermore, we show the results of the proposed model for predicting the behavior of liquids inside containers when one tilts the containers. We also introduce a new dataset of Containers Of liQuid contEnt (COQE) that contains more than 5,000 images of 10,000 liquid containers in context labelled with volume, amount of content, bounding box annotation, and corresponding similar 3D CAD models.

In this paper we propose a method for interactive recognition of household objects using proprioceptive and auditory feedback. In our experiments, the robot observed the changes in its proprioceptive and auditory sensory streams while performing five exploratory behaviors (lift, shake, drop, crush, and push) on 50 common household objects (e.g. bottles, cups, balls, toys, etc.). The robot was tasked with recognizing the objects it was manipulating by feeling them and listening to the sounds that they make without using any visual information. The results show that both proprioception and audio, coupled with exploratory behaviors, can be used successfully for object recognition. Furthermore, the robot was able to integrate feedback from the two modalities, to achieve even better recognition accuracy. Finally, the results show that the robot can boost its recognition rate even further by applying multiple different exploratory behaviors on the object.