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A Probabilistic Framework for Uncertainty-Aware High-Accuracy Precision Grasping of Unknown Objects

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Abstract

Grasping is a fundamental skill for robots which work for manipulation tasks. Grasping of unknown objects remains a big challenge. Precision grasping of unknown objects is even harder. Due to imperfection of sensor measurements and lack of prior knowledge of objects, robots have to handle the uncertainty effectively. In previous work (Chen and Wichert 2015), we use a probabilistic framework to tackle precision grasping of model-based objects. In this paper, we extend the probabilistic framework to tackle the problem of precision grasping of unknown objects. We first propose an object model called probabilistic signed distance function (p-SDF) to represent unknown object surface. p-SDF models measurement uncertainty explicitly and allows measurement from multiple sensors to be fused in real time. Based on the surface representation, we propose a model to evaluate the likelihood of grasp success for antipodal grasps. This model uses four heuristics to model the condition of force closure and perceptual uncertainty. A two step simulated annealing approach is further proposed to search and optimize a precision grasp. We use the object representation as a bridge to unify grasp synthesis and grasp execution. Our grasp execution is performed in a closed-loop, so that robots can actively reduce the uncertainty and react to external perturbations during a grasping process. We perform extensive grasping experiments using real world challenging objects and demonstrate that our method achieves high robustness and accuracy in grasping unknown objects.

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References

  1. Berenson, D., Diankov, R., Nishiwaki, K., Kagami, S., Kuffner, J.: Grasp planning in complex scenes. In: 2007 7th IEEE-RAS International Conference on Humanoid Robots, pp. 42–48. IEEE (2007)

  2. Bicchi, A., Kumar, V.: Robotic grasping and contact: a review. In: ICRA, pp. 348–353. Citeseer (2000)

  3. Bohg, J., Kragic, D.: Learning grasping points with shape context. Robot. Auton. Syst. 58(4), 362–377 (2010)

    Article  Google Scholar 

  4. Bohg, J., Morales, A., Asfour, T., Kragic, D.: Data-driven grasp synthesis - a survey. IEEE Trans. Robot. 30(2), 289–309 (2014). https://doi.org/10.1109/TRO.2013.2289018

    Article  Google Scholar 

  5. Brost, R.C.: Automatic grasp planning in the presence of uncertainty. Int. J. Robot. Res. 7(1), 3–17 (1988)

    Article  Google Scholar 

  6. Chen, D., von Wichert, G.: An uncertainty-aware precision grasping process for objects with unknown dimensions. In: International Conference on 2015 IEEE Robotics and Automation (ICRA), pp. 4312–4317 (2015). https://doi.org/10.1109/ICRA.2015.7139794

  7. Chen, D., von Wichert, G.: An uncertainty-aware precision grasping process for objects with unknown dimensions. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 4312–4317 (2015)

  8. Christopoulos, V.N., Schrater, P.: Handling shape and contact location uncertainty in grasping two-dimensional planar objects. In: IEEE/RSJ International Conference On Intelligent Robots and Systems, 2007. IROS 2007, pp. 1557–1563. IEEE (2007)

  9. 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

    Article  Google Scholar 

  10. Cutkosky, M.R., Howe, R.D.: Human grasp choice and robotic grasp analysis. In: Dextrous Robot Hands, pp. 5–31. Springer (1990)

  11. Dafle, N.C., Rodriguez, A., Paolini, R., Tang, B., Srinivasa, S.S., Erdmann, M., Mason, M.T., Lundberg, I., Staab, H., Fuhlbrigge, T.: Extrinsic dexterity: in-hand manipulation with external forces. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 1578–1585 (2014)

  12. Dang, H., Allen, P.K.: Stable grasping under pose uncertainty using tactile feedback. Auton. Robot. 36(4), 309–330 (2014)

    Article  Google Scholar 

  13. Dietrich, V., Chen, D., Wurm, K., von Wichert, G., Ennen, P.: Probabilistic multi-sensor fusion based on signed distance functions. In: IEEE International Conference on Robotics and Automation (ICRA) (2016)

  14. Dogar, M., Srinivasa, S.: A framework for push-grasping in clutter. In: Proceedings of Robotics: Science and Systems, Los Angeles (2011)

  15. Dragiev, S., Toussaint, M., Gienger, M: Gaussian process implicit surfaces for shape estimation and grasping. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 2845–2850 (2011). https://doi.org/10.1109/ICRA.2011.5980395

  16. Dragiev, S., Toussaint, M., Gienger, M.: Uncertainty aware grasping and tactile exploration. In: 2013 IEEE International Conference On Robotics and Automation (ICRA), pp. 113–119. IEEE (2013)

  17. Eppner, C., Brock, O.: Grasping unknown objects by exploiting shape adaptability and environmental constraints. In: 2013 IEEE/RSJ International Conference On Intelligent Robots and Systems (IROS), pp. 4000–4006. IEEE (2013)

  18. Feix, T., Romero, J., Schmiedmayer, H.B., Dollar, A.M., Kragic, D.: The grasp taxonomy of human grasp types. IEEE Transactions on Human-Machine Systems 46(1), 66–77 (2016)

    Article  Google Scholar 

  19. Felip, J., Morales, A.: Robust sensor-based grasp primitive for a three-finger robot hand. In: IROS 2009. IEEE/RSJ International Conference On Intelligent Robots and Systems, 2009, pp. 1811–1816. IEEE (2009)

  20. Ferrari, C., Canny, J.: Planning optimal grasps. In: Proceedings 1992 IEEE International Conference on Robotics and Automation (1992). https://doi.org/10.1109/ROBOT.1992.219918

  21. Fischinger, D., Vincze, M.: Empty the basket-a shape based learning approach for grasping piles of unknown objects. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2051–2057. IEEE (2012)

  22. Fischinger, D., Weiss, A., Vincze, M.: Learning grasps with topographic features. Int. J. Robot. Res. 34(9), 1167–1194 (2015)

    Article  Google Scholar 

  23. Fischinger, D., Weiss, A., Vincze, M.: Learning grasps with topographic features. I. J. Robotic Res. 34(9), 1167–1194 (2015). https://doi.org/10.1177/0278364915577105

    Article  Google Scholar 

  24. Glover, J., Rus, D., Roy, N.: Probabilistic models of object geometry for grasp planning. In: Proceedings of Robotics: Science and Systems IV, pp. 278–285. Zurich (2008)

  25. Goldberg, K.Y., Mason, M.T.: Bayesian grasping. In: 1990 IEEE International Conference on Robotics and Automation, 1990. Proceedings, pp. 1264–1269. IEEE (1990)

  26. Grundmann, T., Feiten, W., Wichert, G.V.: A gaussian measurement model for local interest point based 6 dof pose estimation. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 2085–2090. IEEE (2011)

  27. Han, L., Trinkle, J.C.: Dextrous manipulation by rolling and finger gaiting. In: 1998 IEEE international conference on robotics and automation, 1998. Proceedings. vol. 1, pp. 730–735. IEEE (1998)

  28. Hang, K., Li, M., Stork, J.A., Bekiroglu, Y., Pokorny, F.T., Billard, A., Kragic, D.: Hierarchical fingertip space: A unified framework for grasp planning and in-hand grasp adaptation. IEEE Transactions on Robotics (2016)

  29. Herzog, A., Pastor, P., Kalakrishnan, M., Righetti, L., Asfour, T., Schaal, S.: Template-based learning of grasp selection. In: 2012 IEEE international conference on robotics and automation (ICRA), pp. 2379–2384 (2012)

  30. Herzog, A., Pastor, P., Kalakrishnan, M., Righetti, L., Bohg, J., Asfour, T., Schaal, S.: Learning of grasp selection based on shape-templates. Auton. Robot. 36(1-2), 51–65 (2014). https://doi.org/10.1007/s10514-013-9366-8

    Article  Google Scholar 

  31. Hsiao, K., Ciocarlie, M., Brook, P.: Bayesian grasp planning. In: ICRA 2011 Workshop on Mobile Manipulation: Integrating Perception and Manipulation (2011)

  32. Huebner, K., Kragic, D.: Selection of robot pre-grasps using box-based shape approximation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008. IROS 2008, pp. 1765–1770. IEEE (2008). https://doi.org/10.1109/IROS.2008.4650722

  33. Ijspeert, A.J., Nakanishi, J., Hoffmann, H., Pastor, P., Schaal, S.: Dynamical movement primitives: learning attractor models for motor behaviors. Neural Computation 25(2), 328–373 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  34. Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: Kinectfusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011)

  35. Jiang, Y., Moseson, S., Saxena, A.: Efficient grasping from RGBD images: Learning using a new rectangle representation. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 3304–3311 (2011). https://doi.org/10.1109/ICRA.2011.5980145

  36. Kehoe, B., Berenson, D., Goldberg, K.: Toward cloud-based grasping with uncertainty in shape: estimating lower bounds on achieving force closure with zero-slip push grasps. In: 2012 IEEE international conference on robotics and automation (ICRA), pp. 576–583. IEEE (2012)

  37. Kopicki, M., Detry, R., Schmidt, F., Borst, C., Stolkin, R., Wyatt, J.L.: Learning dexterous grasps that generalise to novel objects by combining hand and contact models. In: 2014 IEEE International Conference On Robotics and Automation (ICRA), pp. 5358–5365. IEEE (2014)

  38. Lenz, I., Lee, H., Saxena, A.: Deep learning for detecting robotic grasps. Int. J. Robot. Res. 34(4–5), 705–724 (2015)

    Article  Google Scholar 

  39. Li, M., Hang, K., Kragic, D., Billard, A.: Dexterous grasping under shape uncertainty. Robot. Auton. Syst. 75, 352–364 (2016)

    Article  Google Scholar 

  40. Liu, J., Feng, F., Nakamura, Y.C., Pollard, N.S.: A taxonomy of everyday grasps in action. In: 2014 IEEE-RAS international conference on humanoid robots, pp. 573–580 (2014)

  41. Mahler, J., Patil, S., Kehoe, B., van den Berg, J., Ciocarlie, M., Abbeel, P., Goldberg, K.: GP-GPIS-OPT: grasp planning with shape uncertainty using gaussian process implicit surfaces and sequential convex programming. In: 2015 IEEE international conference on robotics and automation (ICRA), pp. 4919–4926 (2015). https://doi.org/10.1109/ICRA.2015.7139882

  42. Murray, R.M., Li, Z., Sastry, S.S., Sastry, S.S.: A Mathematical Introduction to Robotic Manipulation. CRC Press (1994)

  43. Nguyen, V.D.: Constructing force-closure grasps. Int. J. Robot. Res. 7(3), 3–16 (1988)

    Article  Google Scholar 

  44. Paolini, R., Rodriguez, A., Srinivasa, S.S., Mason, M.T.: A data-driven statistical framework for post-grasp manipulation. Int. J. Robot. Res. 33(4), 600–615 (2014)

    Article  Google Scholar 

  45. Pastor, P., Righetti, L., Kalakrishnan, M., Schaal, S.: Online movement adaptation based on previous sensor experiences. In: 2011 IEEE/RSJ International Conference On Intelligent Robots and Systems (IROS), pp. 365–371. IEEE (2011)

  46. Pelossof, R., Miller, A., Allen, P., Jebara, T.: An SVM learning approach to robotic grasping. In: Proceedings. ICRA’04. 2004 IEEE International Conference on Robotics and Automation, 2004, vol. 4, pp. 3512–3518. IEEE (2004)

  47. Pinto, L., Gupta, A.: Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours (2015). arXiv:1509.06825

  48. Rasmussen, C.E.: Gaussian processes for machine learning. MIT Press (2006)

  49. Redmon, J., Angelova, A.: Real-time grasp detection using convolutional neural networks. In: 2015 IEEE International Conference On Robotics and Automation (ICRA), pp. 1316–1322. IEEE (2015)

  50. Reiser, U., Connette, C., Fischer, J., Kubacki, J., Bubeck, A., Weisshardt, F., Jacobs, T., Parlitz, C., Hagele, M., Verl, A.: Care-o-bot3 - creating a product vision for service robot applications by integrating design and technology. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 1992–1998 (2009). https://doi.org/10.1109/IROS.2009.5354526

  51. Roa Garzón, M., Suárez Feijóo, R.: Regrasp planning in the grasp space using independent regions. In: IROS 2009: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1823–1829 (2009)

  52. Weisz, J., Allen, P.K.: Pose error robust grasping from contact wrench space metrics. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 557–562. IEEE (2012)

  53. Williams, O., Fitzgibbon, A.: Gaussian process implicit surfaces. Gaussian Proc in Practice (2007)

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Acknowledgements

We would like to thank our colleges, particularly Florian Wirnshofer, Philip Koehler and Kai Wurm for their useful discussions and suggestions on this work.

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Authors and Affiliations

  1. Corporate Technology, Siemens AG, Otto-Hahn-Ring 6, Munich, Germany

    Dong Chen, Vincent Dietrich, Ziyuan Liu & Georg von Wichert

  2. Chair of Automatic Control Engineering, Technische Universität Müchen, München, Germany

    Dong Chen

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  1. Dong Chen
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  4. Georg von Wichert
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Correspondence to Dong Chen.

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Chen, D., Dietrich, V., Liu, Z. et al. A Probabilistic Framework for Uncertainty-Aware High-Accuracy Precision Grasping of Unknown Objects. J Intell Robot Syst 90, 19–43 (2018). https://doi.org/10.1007/s10846-017-0646-y

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