Haptic Perception of Liquids Enclosed in Containers

Service robots will require several important manipulation skills, including the ability to accurately measure and pour liquids. Prior work on robotic liquid pouring has primarily focused on visual techniques for sensing liquids, but these techniques fall short when liquids are obscured by opaque or closed containers. This paper proposes a complementary method for liquid perception via haptic sensing. The robot moves a container through a series of tilting motions and observes the wrenches induced at the manipulator’s wrist by the liquid’s shifting center of mass. That data is then analyzed with a physics-based model to estimate the liquid’s mass and volume. In experiments, this method achieves error margins of less than lg and 2mL for an unknown liquid in a 600mL cylindrical container. The model can also predict the viscosity of fluids, which can be used for classifying water, oil, and honey with an accuracy of 98%. The estimated volume is used to precisely pour 100mL of water with less than 4% average error.

[1]  Shuhong Liu,et al.  Viscous liquid sloshing damping in cylindrical container using a volume of fluid method , 2009 .

[2]  Oliver Sawodny,et al.  Flow rate control based on differential flatness in automatic pouring robot , 2011, 2011 IEEE International Conference on Control Applications (CCA).

[3]  Brian Mirtich,et al.  Fast and Accurate Computation of Polyhedral Mass Properties , 1996, J. Graphics, GPU, & Game Tools.

[4]  Karl Johan Åström,et al.  BOOK REVIEW SYSTEM IDENTIFICATION , 1994, Econometric Theory.

[5]  Christopher G. Atkeson,et al.  Stereo vision of liquid and particle flow for robot pouring , 2016, 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids).

[6]  Jürgen Sturm,et al.  Tactile object class and internal state recognition for mobile manipulation , 2010, 2010 IEEE International Conference on Robotics and Automation.

[7]  Sethu Vijayakumar,et al.  Active Sequential Learning with Tactile Feedback , 2010, AISTATS.

[8]  Mahmut Reyhanoglu,et al.  Point-to-point liquid container transfer via a PPR robot with sloshing suppression , 2012, 2012 American Control Conference (ACC).

[9]  Wolfram Burgard,et al.  A probabilistic approach to liquid level detection in cups using an RGB-D camera , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[10]  Connor Schenck,et al.  Reasoning About Liquids via Closed-Loop Simulation , 2017, Robotics: Science and Systems.

[11]  Connor Schenck,et al.  Perceiving and reasoning about liquids using fully convolutional networks , 2017, Int. J. Robotics Res..

[12]  Nicholas J. Butko,et al.  Active perception , 2010 .

[13]  M. H. Djavareshkian,et al.  Simulation of Sloshing with the Volume of Fluid Method , 2006 .

[14]  Chonhyon Park,et al.  Robot Motion Planning for Pouring Liquids , 2016, ICAPS.

[15]  H. Huppert,et al.  Pouring viscous fluid out of a tipped container in minimal time. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Michael Beetz,et al.  Envisioning the qualitative effects of robot manipulation actions using simulation-based projections , 2017, Artif. Intell..

[17]  Danica Kragic,et al.  What's in the container? Classifying object contents from vision and touch , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Tsuhan Chen,et al.  Efficient feature extraction for 2D/3D objects in mesh representation , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[19]  V. Constantinescu Slow Viscous Flow , 1995 .

[20]  Norman Hendrich,et al.  Making Sense of Audio Vibration for Liquid Height Estimation in Robotic Pouring , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[21]  Oliver Brock,et al.  Manipulating articulated objects with interactive perception , 2008, 2008 IEEE International Conference on Robotics and Automation.

[22]  Vijay Kumar,et al.  Precise dispensing of liquids using visual feedback , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[23]  Clark R. Dohrmann,et al.  Control for slosh-free motion of an open container , 1997 .

[24]  Dinesh Manocha,et al.  Motion planning for fluid manipulation using simplified dynamics , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[25]  Connor Schenck,et al.  Visual closed-loop control for pouring liquids , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[26]  Peter J. Ramadge,et al.  Learning to identify container contents through tactile vibration signatures , 2016, 2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR).

[27]  R. Ibrahim Liquid Sloshing Dynamics: Theory and Applications , 2005 .

[28]  Carme Torras,et al.  Force-based robot learning of pouring skills using parametric hidden Markov models , 2013, 9th International Workshop on Robot Motion and Control.

[29]  J. Andrew Bagnell,et al.  Interactive segmentation, tracking, and kinematic modeling of unknown 3D articulated objects , 2013, 2013 IEEE International Conference on Robotics and Automation.

[30]  Helge J. Ritter,et al.  Discriminating liquids using a robotic kitchen assistant , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[31]  Markus H. Gross,et al.  Particle-based fluid simulation for interactive applications , 2003, SCA '03.

[32]  Chenfanfu Jiang,et al.  Probabilistic Simulation Predicts Human Performance on Viscous Fluid-Pouring Problem , 2016, CogSci.