Abstract
We present a data-driven approach to simulate the BioTac tactile fingertip sensor within physics engines. The behavior of the sensor is first captured in an experimental setup that records positions and external forces of contacts as well as the sensor output. This data is then used to fit a non-linear model that maps force-annotated mesh collisions of a simulator to sensor responses.
We discuss two deep network architectures that reproduce the BioTac data with high accuracy and demonstrate the simulation of simple grasps with the Shadow Dexterous Hand and five BioTac sensors. We present an open source plug-in for the simulator Gazebo and release the captured dataset alongside this paper.
This research was funded by the German Research Foundation (DFG) and the National Science Foundation of China in project Crossmodal Learning, TRR-169.
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References
Fishel, J.A., Santos, V.J., Loeb, G.E.: A robust micro-vibration sensor for biomimetic fingertips. In: 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 659–663 (2008). https://doi.org/10.1109/BIOROB.2008.4762917
Wettels, N.B.: Biomimetic tactile sensor for object identification and grasp control. University of Southern California (2011)
Fishel, J.A.: Design and use of a biomimetic tactile microvibration sensor with human-like sensitivity and its application in texture discrimination using Bayesian exploration. University of Southern California (2012)
Loeb, G.E.: Estimating point of contact, force and torque in a biomimetic tactile sensor with deformable skin (2013). https://www.syntouchinc.com/wp-content/uploads/2016/12/2013_Lin_Analytical-1.pdf
Chu, V., et al.: Robotic learning of haptic adjectives through physical interaction. Rob. Auton. Syst. 63, 279–292 (2015). https://doi.org/10.1016/j.robot.2014.09.021
Fishel, J.A., Lin, G., Matulevich, B., Loeb, G.: BioTac product manual. SynTouch LLC, V20 edn. (2015). https://www.syntouchinc.com/wp-content/uploads/2017/01/BioTac_Product_Manual.pdf
Shadow Robot Dextrous Hand. https://www.shadowrobot.com/
Koenig, N., Howard, A.: Design and use paradigms for gazebo, an open-source multi-robot simulator. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Proceedings, vol. 3, pp. 2149–2154. IEEE (2004). https://doi.org/10.1109/iros.2004.1389727
Quigley, M., Conley, K., Gerkey, B. P., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng., A. Y., ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)
Open Dynamics Engine. http://www.ode.org/
Bullet Physics Library. http://bulletphysics.org/wordpress/
Simbody Multibody Physics API. https://simtk.org/projects/simbody/
Dynamic Animation and Robotics Toolkit. http://dartsim.github.io/
Cyberbotics Inc., Webots robot simulator. https://www.cyberbotics.com/
Coppelia Robotics, V-REP Virtual Robot Experimentation Platform. http://www.coppeliarobotics.com/
Cutkosky, M.R.: On grasp choice, grasp models, and the design of hands for manufacturing tasks. IEEE Trans. Rob. Autom. 5(3), 269–279 (1989). https://doi.org/10.1109/70.34763
Bicchi, A., Kumar, V.: Robotic grasping and contact: a review. In: IEEE International Conference on Robotics and Automation, Proceedings, ICRA 2000, vol. 1, pp. 348–353. IEEE (2000). https://doi.org/10.1109/ROBOT.2000.844081
Miller, A.T., Allen, P.K.: Graspit! a versatile simulator for robotic grasping. IEEE Rob. Autom. Mag. 11(4), 110–122 (2004). https://doi.org/10.1109/MRA.2004.1371616
Ciocarlie, M.T., Allen, P.K.: Hand posture subspaces for dexterous robotic grasping. Int. J. Rob. Res. 28(7), 851–867 (2009). https://doi.org/10.1177/0278364909105606
Goldfeder, C., Ciocarlie, M., Dang, H., Allen, P.K.: The columbia grasp database. In: IEEE International Conference on Robotics and Automation, ICRA 2009, pp. 1710–1716. IEEE (2009). https://doi.org/10.1109/ROBOT.2009.5152709
Taylor, J.R., Drumwright, E.M., Hsu, J.: Analysis of grasping failures in multi-rigid body simulations. In: IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), pp. 295–301. IEEE (2016). https://doi.org/10.1109/SIMPAR.2016.7862410
Scharfe, H., Hendrich, N., Zhang, J.: Hybrid physics simulation of multi-fingered hands for dexterous in-hand manipulation. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3777–3783. IEEE (2012). https://doi.org/10.1109/ICRA.2012.6225156
León, B., et al.: OpenGRASP: a toolkit for robot grasping simulation. In: International Conference on Simulation, Modeling, and Programming for Autonomous Robots, pp. 109–120. Springer (2010). https://doi.org/10.1007/978-3-642-17319-6_13
OpenGRASP toolkit. http://opengrasp.sourceforge.net/
Diankov, R., Kuffner, J.: OpenRAVE: a planning architecture for autonomous robotics. Robotics Institute, Pittsburgh, PA, Technical report. CMU-RI-TR-08-34 79 (2008)
Dahiya, R.S., Metta, G., Valle, M., Sandini, G.: Tactile sensing-from humans to humanoids. IEEE Trans. Rob. 26(1), 1–20 (2010). https://doi.org/10.1109/TRO.2009.2033627
Yousef, H., Boukallel, M., Althoefer, K.: Tactile sensing for dexterous in-hand manipulation in robotics-a review. Sens. Actuators A Phys. 167(2), 171–187 (2011). https://doi.org/10.1016/j.sna.2011.02.038
Message and service publishers for interfacing with Gazebo through ROS. http://wiki.ros.org/gazebo_ros
Optoforce Ltd., 3D Force Sensor. https://optoforce.com/3d-force-sensor-omd
Grazioso, S., Sonneville, V., Di Gironimo, G., Bauchau, O., Siciliano, B.: A nonlinear finite element formalism for modelling flexible and soft manipulators. In: IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), pp. 185–190. IEEE (2016). https://doi.org/10.1109/SIMPAR.2016.7862394
ANSYS multiphysics simulation. https://www.ansys.com/products/platform/multiphysics-simulation
Comsol Multiphysics suite. https://www.comsol.com/multiphysics
ATI Industrial Automation Inc.: F/T Sensor Nano17. http://www.ati-ia.com/products/ft/ft_models.aspx?id=Nano17
Olson, E.: AprilTag: a robust and flexible visual fiducial system. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3400–3407. IEEE (2011). https://doi.org/10.1109/ICRA.2011.5979561
Ciobanu, V., Popescu, D, Petrescu, A.: Point of contact location and normal force estimation using biomimetical tactile sensors. In: Eighth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), pp. 373–378. IEEE (2014). https://doi.org/10.1109/CISIS.2014.52
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Ruppel, P., Jonetzko, Y., Görner, M., Hendrich, N., Zhang, J. (2019). Simulation of the SynTouch BioTac Sensor. In: Strand, M., Dillmann, R., Menegatti, E., Ghidoni, S. (eds) Intelligent Autonomous Systems 15. IAS 2018. Advances in Intelligent Systems and Computing, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-01370-7_30
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