Rapid Flow Cup-Enabled Liquid Perception Using a Position-Based Physics Simulator for Robotic Liquid Manipulation

Accurate robotic liquid manipulation has been a challenging task for many industry sectors, which requires the robot to sufficiently understand the liquid flow behaviour. Physics simulation-informed liquid perception and visual or tactile-based direct liquid sensing have been explored as useful approaches for liquid manipulation. However, these approaches may not be practical to implement in some resource and space-limited cases as they require a physical robot to learn the liquid flow behaviour through physical interaction with the liquid. This paper proposes a liquid perception framework where a low-cost and robot-free flow cup test (a standard fluidity characterisation test) is utilised to capture the physical liquid flow behaviour which is then transformed into a position-based physics simulation during a virtual flow cup test using Bayesian optimisation. Such liquid perception through the ‘real-to-sim’ flow cup test is intended for guiding robot operations in any subsequent liquid manipulation tasks. The viability of the proposed framework is examined by comparing the physical liquid manipulation performance (i.e., accuracy) with that in simulation using the learned liquid flow behaviour. A robotic crack sealing experiment is implemented as a validation use case in manufacturing. The results suggest that the proposed framework is able to capture and predict a random liquid flow behaviour at a statistical mean accuracy of 85.4-87.0% (with a high-probability accuracy of 88-90%) in approx. 6 minutes using the computation resource in this study, validating its feasibility for underpinning general robotic liquid manipulation applications. Note to Practitioners—This article investigates a low-cost, agile liquid flow perception method for robotic manipulators, establishing an understanding of a liquid flow behaviour without the presence of a physical robot. The advantage of the proposed method lies in 1) its universality for different liquids and manipulation tasks using rapid physics simulation of standard fluidity characterisation tests; and 2) the simplicity and low investment in the system configuration where no anterior physical interactions are required between a manipulator and the target liquid as existed in the literature. This makes the proposed method highly deployable and useful for some resource and space-limited scenarios, and can support parallel perception of multiple liquids for asynchronous tasks of different manipulators. For practicability, the proposed flow cup test setup can either be modified into an integrated flow perception unit in field robots, or more promisingly, be variable and/or coupled with other standard tests to establish a systematic virtual liquid benchmarking test family stimulating data-driven liquid manipulation policies for factory-based manipulators to ensure effectiveness and flexibility.

Authors

Publication Details

Journal
Apollo (University of Cambridge)
Published
2026-01-01
DOI
https://doi.org/10.17863/cam.124577
Primary Topic
Soft Robotics and Applications
Type
article
Field-Weighted Citation Impact
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article

Rapid Flow Cup-Enabled Liquid Perception Using a Position-Based Physics Simulator for Robotic Liquid Manipulation

Samuel Schaefer, Damian Palin, Abir Al-Tabbaa, Fumiya Iida
Apollo (University of Cambridge)
Soft Robotics and Applications
article

Rapid Flow Cup-Enabled Liquid Perception Using a Position-Based Physics Simulator for Robotic Liquid Manipulation

Samuel Schaefer, Damian Palin, Abir Al-Tabbaa, Fumiya Iida
article en

Abstract

Accurate robotic liquid manipulation has been a challenging task for many industry sectors, which requires the robot to sufficiently understand the liquid flow behaviour. Physics simulation-informed liquid perception and visual or tactile-based direct liquid sensing have been explored as useful approaches for liquid manipulation. However, these approaches may not be practical to implement in some resource and space-limited cases as they require a physical robot to learn the liquid flow behaviour through physical interaction with the liquid. This paper proposes a liquid perception framework where a low-cost and robot-free flow cup test (a standard fluidity characterisation test) is utilised to capture the physical liquid flow behaviour which is then transformed into a position-based physics simulation during a virtual flow cup test using Bayesian optimisation. Such liquid perception through the ‘real-to-sim’ flow cup test is intended for guiding robot operations in any subsequent liquid manipulation tasks. The viability of the proposed framework is examined by comparing the physical liquid manipulation performance (i.e., accuracy) with that in simulation using the learned liquid flow behaviour. A robotic crack sealing experiment is implemented as a validation use case in manufacturing. The results suggest that the proposed framework is able to capture and predict a random liquid flow behaviour at a statistical mean accuracy of 85.4-87.0% (with a high-probability accuracy of 88-90%) in approx. 6 minutes using the computation resource in this study, validating its feasibility for underpinning general robotic liquid manipulation applications. Note to Practitioners—This article investigates a low-cost, agile liquid flow perception method for robotic manipulators, establishing an understanding of a liquid flow behaviour without the presence of a physical robot. The advantage of the proposed method lies in 1) its universality for different liquids and manipulation tasks using rapid physics simulation of standard fluidity characterisation tests; and 2) the simplicity and low investment in the system configuration where no anterior physical interactions are required between a manipulator and the target liquid as existed in the literature. This makes the proposed method highly deployable and useful for some resource and space-limited scenarios, and can support parallel perception of multiple liquids for asynchronous tasks of different manipulators. For practicability, the proposed flow cup test setup can either be modified into an integrated flow perception unit in field robots, or more promisingly, be variable and/or coupled with other standard tests to establish a systematic virtual liquid benchmarking test family stimulating data-driven liquid manipulation policies for factory-based manipulators to ensure effectiveness and flexibility.

Apollo (University of Cambridge)
Industry, innovation and infrastructure
Openalex Percentile: Top 92%
Soft Robotics and Applications
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