Discriminating liquids using a robotic kitchen assistant

Elbrechter, Christof; Maycock, Jonathan; Haschke, Robert; Ritter, Helge

doi:10.1109/iros.2015.7353449

Cited by 13 publications

(8 citation statements)

References 13 publications

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“…Research in this field has been progressing at a good pace, being this the reason for which several AE already implements adaptive control strategies. For instance, some researchers created CV procedures to classify liquids of different viscosity that are difficult to distinguish (using images of the liquid surface, in motion, as input to nearest neighbor and polynomial regression classifiers) [242] as well as to learn dynamical models of liquid flow (by applying the Lucas-Kanade CV method to images of two stereo cameras, for 2D liquid flow estimation, and a tailored 3D reconstruction method to obtain the 3D flow) [243], to adapt parameters in the control of pouring actions. Expectation Maximization-based Reinforcement learning (RL) was used by others to learn policies for tossing food and to, more generally, allow a robot to acquire new motor skills from Dynamic Movement Primitives encoding the skill demonstration by a human [209].…”

Section: G Artificial Intelligence For Foodservice Roboticsmentioning

confidence: 99%

Towards Foodservice Robotics: a taxonomy of actions of foodservice workers and a critical review of supportive technology

Pereira¹,

Bozzato²,

Dario³

et al. 2021

Preprint

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This paper was motivated by a critical need in foodservice: the ability to produce consistent and high-quality meals ad-hoc, without overloading the workers or harming their health. Robotic and autonomous systems are promising technologies to solve this. However, there is not a unified framework in robotics research focused on the professional foodservice environment. This paper provides two tools for researchers and engineers in this field: (i) a taxonomy of basic actions that foodservice workers perform during their physical tasks; and (ii) a systematic review of mechatronic systems being developed or already in use in foodservice. The taxonomy can be immediately useful to divide research and development by the classes of actions. In addition, we found specific categories of actions that have been rarely automated so far and need further investigation. The results of our review can be readily applied in industry, too: presently, most equipment is a custom-built machine with limited adaptiveness; when systems include industrial robots, cobots are being preferred; the implementation of collaborative operations between humans and robots is not common yet and its applicability may be suitable only for certain contexts; finally, we identify scientific publications introducing adaptive control strategies and movement policies for some actions that can be implemented today to achieve a more robust actuation.

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Section: G Artificial Intelligence For Foodservice Roboticsmentioning

confidence: 99%

Towards Foodservice Robotics: a taxonomy of actions of foodservice workers and a critical review of supportive technology

Pereira¹,

Bozzato²,

Dario³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Estimation of physical fluid properties: There are also methods that don't assume any underlying model at all, and directly estimate real-physical parameters of liquids from data using robots. For example, to obtain the volume [22], height of the body of liquid [23], 3D shape of the container [24] and dynamic viscosity [25], [26], [27]. These methods exploit special measurement equipment such as RGBD cameras, microphones or tactile sensors for parameter estimation.…”

Section: Robots Interacting With Fluidsmentioning

confidence: 99%

“…Estimation of physical properties: A few approaches focus on estimating physical parameters such as volume [3] and viscosity [4,11], These methods exploit special measurement equipment such as RGBD cameras or tactile sensors for parameter estimation. In our context, knowledge of the physical parameters would only be useful if a high-fidelity simulation is used to optimize decision-making during manipulation of fluids.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Stir to Pour: Efficient Calibration of Liquid Properties for Pouring Actions

Tatiana

Pucci

Taylor

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Humans use simple probing actions to develop intuition about the physical behavior of common objects. Such intuition is particularly useful for adaptive estimation of favorable manipulation strategies of those objects in novel contexts. For example, observing the effect of tilt on a transparent bottle containing an unknown liquid provides clues on how the liquid might be poured. It is desirable to equip general-purpose robotic systems with this capability because it is inevitable that they will encounter novel objects and scenarios. In this paper, we teach a robot to use a simple, specified probing strategystirring with a stick-to reduce spillage when pouring unknown liquids. In the probing step, we continuously observe the effects of a real robot stirring a liquid, while simultaneously tuning the parameters to a model (simulator) until the two outputs are in agreement. We obtain optimal simulation parameters, characterizing the unknown liquid, via a Bayesian Optimizer that minimizes the discrepancy between real and simulated outcomes. Then, we optimize the pouring policy conditioning on the optimal simulation parameters determined via stirring. We show that using stirring as a probing strategy result in reduced spillage for three qualitatively different liquids when executed on a UR10 Robot, compared to probing via pouring. Finally, we provide quantitative insights into the reason for stirring being a suitable calibration task for pouring -a step towards automatic discovery of probing strategies.

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“…However, in their work no perception of the liquid is used. Regarding the area of liquid perception, Elbrechter et al [5] focus on the problem of detecting liquid viscosity. Morris and Kutulakos [6] look at reconstructing a refractive surface, but this requires a pattern placed underneath the liquid surface.…”

Section: Related Workmentioning

confidence: 99%

Accurate Pouring with an Autonomous Robot Using an RGB-D Camera

Burgard

2018

Advances in Intelligent Systems and Computing

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Robotic assistants in a home environment are expected to perform various complex tasks for their users. One particularly challenging task is pouring drinks into cups, which for successful completion, requires the detection and tracking of the liquid level during a pour to determine when to stop. In this paper, we present a novel approach to autonomous pouring that tracks the liquid level using an RGB-D camera and adapts the rate of pouring based on the liquid level feedback. We thoroughly evaluate our system on various types of liquids and under different conditions, conducting over 250 pours with a PR2 robot. The results demonstrate that our approach is able to pour liquids to a target height with an accuracy of a few millimeters.

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Discriminating liquids using a robotic kitchen assistant

Cited by 13 publications

References 13 publications

Towards Foodservice Robotics: a taxonomy of actions of foodservice workers and a critical review of supportive technology

Towards Foodservice Robotics: a taxonomy of actions of foodservice workers and a critical review of supportive technology

Stir to Pour: Efficient Calibration of Liquid Properties for Pouring Actions

Accurate Pouring with an Autonomous Robot Using an RGB-D Camera

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