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A Tangible Interface for Transferring Skills

Using Perception and Projection Capabilities in Human-Robot Collaboration Tasks

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Abstract

Our research focuses on exploring new modalities to make robots acquire skills in a fast and user-friendly manner. In this work we present a novel active interface with perception and projection capabilities for simplifying the skill transfer process. The interface allows humans and robots to interact with each other in the same environment, with respect to visual feedback. During the learning process, the real workspace is used as a tangible interface for helping the user to better understand what the robot has learned up to then, to display information about the task or to get feedback and guidance. Thus, the user is able to incrementally visualize and assess the learner’s state and, at the same time, focus on the skill transfer without disrupting the continuity of the teaching interaction. We also propose a proof-of-concept, as a core element of the architecture, based on an experimental setting where a pico-projector and an rgb-depth sensor are mounted onto the end-effector of a 7-DOF robotic arm.

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References

  1. Alenyà G, Dellen B, Torras C (2011) 3D modelling of leaves from color and ToF data for robotized plant measuring. In: Proceedings of IEEE international conference on robotics and automation (ICRA), pp 3408–3414

    Google Scholar 

  2. Aleotti J, Caselli S (2006) Grasp recognition in virtual reality for robot pregrasp planning by demonstration. In: Proceedings of IEEE international conference on robotics and automation (ICRA), pp 2801–2806

    Google Scholar 

  3. Bimber O, Raskar R (2005) Spatial augmented reality: a modern approach to augmented reality. In: Proceedings of annual conference on computer graphics and interactive techniques (SIGGRAPH). ACM, New York

    Google Scholar 

  4. Calinon S, Billard A (2007) What is the teacher’s role in robot programming by demonstration? Toward benchmarks for improved learning. Interact Stud 8(3):441–464 (Special issue on psychological benchmarks in human-robot interaction)

    Google Scholar 

  5. Calinon S, Guenter F, Billard A (2007) On learning, representing and generalizing a task in a humanoid robot. IEEE Trans Syst Man Cybern, Part B, Cybern 37(2):286–298

    Article  Google Scholar 

  6. Calinon S, Sardellitti I, Caldwell DG (2010) Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies. In: Proc IEEE/RSJ intl conf on intelligent robots and systems (IROS), Taipei, Taiwan, pp 249–254

    Google Scholar 

  7. Chernova S, Veloso M (2010) Confidence-based multi-robot learning from demonstration. Int J Soc Robot 2(2):195–215

    Article  Google Scholar 

  8. Delson N, West H (1994) Robot programming by human demonstration: the use of human variation in identifying obstacle free trajectories. In: Proceedings of IEEE international conference on robotics and automation (ICRA), pp 564–571

    Google Scholar 

  9. Dourish P (2001) Where the action is: the foundations of embodied interaction. MIT Press, Cambridge

    Google Scholar 

  10. Ekvall S, Kragic D (2006) Learning task models from multiple human demonstrations. In: The 15th IEEE international symposium on robot and human interactive communication (ROMAN), pp 358–363

    Chapter  Google Scholar 

  11. Faugeras OD, Toscani G (1986) The calibration problem for stereo. In: Proceeding of the IEEE computer society conference on computer vision and pattern recognition (CVPR), pp 15–20

    Google Scholar 

  12. Featherstone R, Orin DE, (2008) Dynamics. In: Siciliano B, Khatib OO (eds) Handbook of robotics. Springer, Secaucus, pp 35–65

    Chapter  Google Scholar 

  13. Friedrich H, Muench S, Dillmann R, Bocionek S, Sassin M (1996) Robot programming by demonstration (RPD): supporting the induction by human interaction. Mach Learn 23(2):163–189

    Google Scholar 

  14. Gillet A, Sanner M, Stoffler D, Olson A (2005) Tangible augmented interfaces for structural molecular biology. IEEE Comput Graph Appl 25:13–17

    Article  Google Scholar 

  15. Greenfield P (1984) Theory of the teacher in learning activities of everyday life. In: Rogoff B, Lave J (eds) Everyday cognition: its development in social context. Harvard University Press, Cambridge

    Google Scholar 

  16. Harrison C, Benko H, Wilson AD (2011) Omnitouch: wearable multitouch interaction everywhere. In: Proceedings of the 24th annual ACM symposium on user interface software and technology (UIST). ACM, New York, pp 441–450

    Google Scholar 

  17. Hosoi K, Dao VN, Mori A, Sugimoto M (2007) Visicon: a robot control interface for visualizing manipulation using a handheld projector. In: Proceedings of the international conference on advances in computer entertainment technology. ACM, New York, pp 99–106

    Chapter  Google Scholar 

  18. Ishii H, Ullmer B (1997) Tangible bits: towards seamless interfaces between people, bits and atoms. In: Proceedings of the SIGCHI conference on human factors in computing systems (CHI). ACM, New York, pp 234–241

    Chapter  Google Scholar 

  19. Ito M, Noda K, Hoshino Y, Tani J (2006) Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model. Neural Netw 19(3):323–337

    Article  MATH  Google Scholar 

  20. Kazuki K, Seiji Y (2010) Extending commands embedded in actions for human-robot cooperative tasks. Int J Soc Robot 2(2):159–173

    Article  Google Scholar 

  21. Kendall DG (1989) A survey of the statistical theory of shape. Stat Sci 4(2):87–99

    Article  MathSciNet  MATH  Google Scholar 

  22. Kheddar A (2001) Teleoperation based on the hidden robot concept. IEEE Trans Syst Man Cybern, Part A, Syst Hum 31(1):1–13

    Article  Google Scholar 

  23. Klemmer SR, Hartmann B, Takayama L (2006) How bodies matter: five themes for interaction design. In: Proceedings of the 6th conference on designing interactive systems (DIS). ACM, New York, pp 140–149

    Chapter  Google Scholar 

  24. Kuniyoshi Y, Inaba M, Inoue H (1989) Teaching by showing: Generating robot programs by visual observation of human performance. In: Proceedings intl symposium of industrial robots, Tokyo, Japan, pp 119–126

    Google Scholar 

  25. Lauria S, Bugmann G, Kyriacou T, Klein E (2002) Mobile robot programming using natural language. Robot Auton Syst 38(3–4):171–181

    Article  Google Scholar 

  26. Linder N, Maes P (2010) LuminAR: portable robotic augmented reality interface design and prototype. In: Adjunct proceedings of the 23nd annual ACM symposium on user interface software and technology (UIST). ACM, New York, pp 395–396

    Chapter  Google Scholar 

  27. Luh JYS, Walker MW, Paul RPC (1980) Resolved-acceleration control of mechanical manipulators. IEEE Trans Autom Control 25:468–474

    Article  MATH  Google Scholar 

  28. Nicolescu MN, Mataric MJ (2003) Natural methods for robot task learning: instructive demonstrations, generalization and practice. In: Second international joint conference on autonomous agents and Multi-Agents and Multi-Agent systems, Melbourne, Australia

    Google Scholar 

  29. Ogawara K, Takamatsu J, Kimura H, Ikeuchi K (2003) Extraction of essential interactions through multiple observations of human demonstrations. In: IEEE transactions on industrial joint Conference on autonomous agents and multiagent systems, pp 667–675

    Google Scholar 

  30. Okada K, Kino Y, Inaba M, Inoue H (2003) Visually-based humanoid remote control system under operator’s assistance and its application to object manipulation. In: Proc IEEE intl conf on humanoid robots (Humanoids), Karlsruhe, Germany

    Google Scholar 

  31. OpenKinect: Kinect calibrations. Online available at http://openkinect.org/wiki/Calibration

  32. OpenNI (User Guide (2010)). Online available at http://openni.org/documentation

  33. Park J, Kim GJ (2009) Robots with projectors: an alternative to anthropomorphic HRI. In: Proceedings of the 4th ACM/IEEE international conference on human robot interaction (HRI). ACM, New York, pp 221–222

    Chapter  Google Scholar 

  34. Primesense NITE 1.3 algorithms notes (2010). Online available at http://primesense.com

  35. Rusu RB, Cousins S (2011) 3D is here: point cloud library PCL. In: Proceedings of IEEE international conference on robotics and automation (ICRA), Shanghai, China

    Google Scholar 

  36. Schöning J, Rohs M, Kratz S, Löchtefeld M, Krüger A (2009) Map torchlight: a mobile augmented reality camera projector unit. In: Adjunct proceedings of the 27th international conference on human factors in computing systems (CHI EA). ACM, New York, pp 3841–3846

    Google Scholar 

  37. Segre AM (1988) Machine learning of robot assembly plans. Kluwer Academic, Norwell

    Book  Google Scholar 

  38. Shotton J, Fitzgibbon AW, Cook M, Sharp T, Finocchio M, Moore R, Kipman A, Blake A (2011) Real-time human pose recognition in parts from single depth images. In: Proceeding of the IEEE computer society conference on computer vision and pattern recognition (CVPR), pp 1297–1304

    Google Scholar 

  39. Song H, Grossman T, Fitzmaurice G, Guimbretiere F, Khan A, Attar R, Kurtenbach G (2009) Penlight: combining a mobile projector and a digital pen for dynamic visual overlay. In: Proceedings of the 27th international conference on human factors in computing systems (CHI). ACM, New York, pp 143–152

    Chapter  Google Scholar 

  40. Thomaz AL, Breazeal C (2008) Teachable robots: understanding human teaching behavior to build more effective robot learners. Artif Intell 172(6–7):716–737

    Article  Google Scholar 

  41. Vogel C, Poggendorf M, Walter C, Elkmann N (2011) Towards safe physical human-robot collaboration: a projection-based safety system. In: Proceedings of international conference on intelligent robots and systems IEEE/RSJ, San Francisco, CA, USA, pp 3355–3360

    Google Scholar 

  42. Voyles R, Khosla P (1998) A multi-agent system for programming robotic agents by human demonstration. In: AI and manufacturing research planning workshop

    Google Scholar 

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Authors and Affiliations

  1. Department of Advanced Robotics, Istituto Italiano di Tecnologia, via Morego, 30, 16163, Genova, Italy

    Davide De Tommaso, Sylvain Calinon & Darwin G. Caldwell

Authors
  1. Davide De Tommaso
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  2. Sylvain Calinon
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  3. Darwin G. Caldwell
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Correspondence to Davide De Tommaso.

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De Tommaso, D., Calinon, S. & Caldwell, D.G. A Tangible Interface for Transferring Skills. Int J of Soc Robotics 4, 397–408 (2012). https://doi.org/10.1007/s12369-012-0154-y

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