Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations

IEEE International Conference on Automation Science and Engineering

Volumn 2016-November, Issue , 2016, Pages 827-834

  Laskey, Michael ^a   Lee, Jonathan ^a   Chuck, Caleb ^a   Gealy, David ^a   Hsieh, Wesley ^a   Pokorny, Florian T ^a   Dragan, Anca D ^a   Goldberg, Ken ^a,b  

^a Department of Electrical Engineering and Computer Sciences ^*  (United States)

^b DEPARTMENT OF INDUSTRIAL ENGINEERING AND OPERATIONS RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUTTER (INFORMATION THEORY); COST FUNCTIONS; DEMONSTRATIONS; ROBOTS; SUPERVISORY PERSONNEL;

DEEP LEARNING; LEARNING FROM DEMONSTRATION; ONLINE LEARNING; ORDER FULFILLMENT; PHYSICAL EXPERIMENTS; ROBOT CONTROLS; ROBOT GRASPING; SYSTEM DYNAMICS;

ROBOT PROGRAMMING;

EID: 85000982103     PISSN: 21618070     EISSN: 21618089     Source Type: Conference Proceeding    
DOI: 10.1109/COASE.2016.7743488     Document Type: Conference Paper

References (35)

1. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, X. Zheng, TensorFlow: Large-scale machine learning on heterogeneous systems, 2015, software available from tensorflow. org. [Online]. Available: http://tensorflow. org/
2. P. Abbeel, A. Coates, M. Quigley, A. Y. Ng, An application of reinforcement learning to aerobatic helicopter flight, NIPS, vol. 19, p. 1, 2007.
3. P. Abbeel, D. Dolgov, A. Y. Ng, S. Thrun, Apprenticeship learning for motion planning with application to parking lot navigation, in IROS 2008. IEEE/RS. IEEE.
4. B. D. Argall, S. Chernova, M. Veloso, B. Browning, A survey of robot learning from demonstration, Robotics, autonomous systems, vol. 57, no. 5, pp. 469-483, 2009.
5. Y. Bengio, J. Louradour, R. Collobert, J. Weston, Curriculum learning, in Proceedings of the 26th annual international conference on machine learning. ACM, 2009, pp. 41-48.
6. A. Bicchi, V. Kumar, Robotic grasping, contact: A review, in ICRA. Citeseer, 2000, pp. 348-353.
7. G. Bradski, Dr. Dobb's Journal of Software Tools, 2000.
8. S. Chernova, M. Veloso, Interactive policy learning through confidence-based autonomy, Journal of Artificial Intelligence Research, vol. 34, no. 1, p. 1, 2009.
9. A. Cosgun, T. Hermans, V. Emeli, M. Stilman, Push planning for object placement on cluttered table surfaces, in Intelligent Robots, Systems (IROS), 2011 IEEE/RSJ International Conference on. IEEE, 2011, pp. 4627-4632.
10. M. Dogar, K. Hsiao, M. Ciocarlie, S. Srinivasa, Physics-based grasp planning through clutter, Robotics: Science, systems VIII, 2012.
11. M. Dogar, S. Srinivasa, A framework for push-grasping in clutter, Robotics: Science, systems VII, 2011.
12. F. Duvallet, T. Kollar, A. Stentz, Imitation learning for natural language direction following through unknown environments, in ICRA. IEEE, 2013, pp. 1047-1053.
13. D. H. Grollman, O. C. Jenkins, Dogged learning for robots, in ICRA, 2007 IEEE.
14. X. Guo, S. Singh, H. Lee, R. L. Lewis, X. Wang, Deep learning for real-time atari game play using offline monte-carlo tree search planning, in NIPS, 2014, pp. 3338-3346.
15. K. Judah, A. Fern, T. Dietterich, Active imitation learning via state queries, in Proceedings of the ICML Workshop on Combining Learning Strategies to Reduce Label Cost, 2011.
16. D. Katz, J. Kenney, O. Brock, How can robots succeed in unstructured environments, in In Workshop on Robot Manipulation: Intelligence in Human Environments at Robotics: Science, Systems. Citeseer, 2008.
17. B. Kim, J. Pineau, Maximum mean discrepancy imitation learning. " in Robotics Science, Systems, 2013.
18. J. E. King, J. A. Haustein, S. S. Srinivasa, T. Asfour, Nonprehensile whole arm rearrangement planning on physics manifolds, ICRA 2015 IEEE.
19. N. Kitaev, I. Mordatch, S. Patil, P. Abbeel, Physics-based trajectory optimization for grasping in cluttered environments, pp. 3102-3109, 2015.
20. A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, in Advances in neural information processing systems, 2012, pp. 1097-1105.
21. M. Laskey, S. Staszak, W. Y.-S. Hsieh, J. Mahler, F. T. Pokorny, A. D. Dragan, K. Goldberg, Shiv: Reducing supervisor burden in dagger using support vectors for efficient learning from demonstrations in high dimensional state spaces, in 2016 IEEE International Conference on Robotics, Automation (ICRA). IEEE, 2016, pp. 462-469.
22. A. E. Leeper, K. Hsiao, M. Ciocarlie, L. Takayama, D. Gossow, Strategies for human-in-the-loop robotic grasping, in Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction. ACM, 2012, pp. 1-8.
23. S. Levine, C. Finn, T. Darrell, P. Abbeel, End-to-end training of deep visuomotor policies, arXiv preprint arXiv:1504. 00702, 2015.
24. M. T. Mason, Mechanics, planning of manipulator pushing operations, The International Journal of Robotics Research, vol. 5, no. 3, pp. 53-71, 1986.
25. M. Nieuwenhuisen, D. Droeschel, D. Holz, J. Stuckler, A. Berner, J. Li, R. Klein, S. Behnke, Mobile bin picking with an anthropomorphic service robot, in Robotics, Automation (ICRA), 2013 IEEE International Conference on. IEEE, 2013, pp. 2327-2334.
26. L. Pinto, A. Gupta, Supersizing self-supervision: Learning to grasp from 50k tries, 700 robot hours, arXiv preprint arXiv:1509. 06825, 2015.
27. S. Ross, D. Bagnell, Efficient reductions for imitation learning, in International Conference on Artificial Intelligence, Statistics, 2010, pp. 661-668.
28. S. Ross, G. J. Gordon, J. A. Bagnell, A reduction of imitation learning, structured prediction to no-regret online learning, arXiv preprint arXiv:1011. 0686, 2010.
29. S. Ross, N. Melik-Barkhudarov, K. S. Shankar, A. Wendel, D. Dey, J. A. Bagnell, M. Hebert, Learning monocular reactive uav control in cluttered natural environments, in ICRA, 2013 IEEE. IEEE.
30. T. D. Sanger, Neural network learning control of robot manipulators using gradually increasing task difficulty, Robotics, Automation, IEEE Transactions on, vol. 10, no. 3, pp. 323-333, 1994.
31. B. Schölkopf, A. J. Smola, Learning with kernels: Support vector machines, regularization, optimization, beyond. MIT press, 2002.
32. M. Stilman, J.-U. Schamburek, J. Kuffner, T. Asfour, Manipulation planning among movable obstacles, in Robotics, Automation, 2007 IEEE International Conference on. IEEE, 2007, pp. 3327-3332.
33. R. S. Sutton, A. G. Barto, Reinforcement learning: An introduction. MIT press, 1998.
34. J. Van Den Berg, S. Miller, D. Duckworth, H. Hu, A. Wan, X.-Y. Fu, K. Goldberg, P. Abbeel, Superhuman performance of surgical tasks by robots using iterative learning from human-guided demonstrations, in ICRA, 2010 IEEE. IEEE, 2010, pp. 2074-2081.
35. J. Van Den Berg, M. Stilman, J. Kuffner, M. Lin, D. Manocha, Path planning among movable obstacles: A probabilistically complete approach, in Algorithmic Foundation of Robotics VIII. Springer, 2009, pp. 599-614.