Can we learn finite state machine robot controllers from interactive demonstration?

Studies in Computational Intelligence

Volumn 264, Issue , 2010, Pages 407-430

  Grollman, Daniel H ^a   Jenkins, Odest Chadwicke ^a  

^a BROWN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 74049085386     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-05181-4_17     Document Type: Conference Paper

References (56)

1. Snelson, E., Ghahramani, Z.: Sparse Gaussian Processes using Pseudo-inputs. In: NIPS, Vancouver, CAN, December 2006, pp. 1257-1264 (2006)
2. Dasgupta, S., Hsu, D., Monteleoni, C.: A general agnostic active learning algorithm. In: NIPS, Vancouver, CAN, December 2007, pp. 353-360 (2007)
3. Calinon, S., Billard, A.: A Probabilistic Programming by Demonstration Framework Handling Constraints in Joint Space and Task Space. In: IROS, Nice, France, September 2008, pp. 367-372 (2008)
4. Abbeel, P., Coates, A., Quigley, M., Ng, A.Y.: An application of reinforcement learning to aerobatic helicopter flight. In: Neural Information Processing Systems [1], pp. 1-8
5. Adams, J.A., Rani, P., Sarkar, N.:Mixed initiative interaction and robotic systems. Technical Report WS-04-10, Vanderbilt University (2004)
6. Atkeson, C., Schaal, S.: Robot learning from demonstration. In: International Conference on Machine Learning, Nashville, TN, July 1997, pp. 12-20 (1997)
7. Bentivegna, D.C., Atkeson, C.G., Cheng, G.: Learning tasks from observation and practice. Robotics and Autonomous Systems 47(2-3), 163-169 (2004)
8. Bonilla, E.V., Chai, K.M.A., Williams, C.K.I.: Multi-task Gaussian process prediction. In: Neural Information Processing Systems [2], pp. 153-160
9. Breazeal, C., Berlin, M., Brooks, A.G., Gray, J., Thomaz, A.L.: Using perspective taking to learn from ambiguous demonstrations. Robotics and Autonomous Systems 54(5), 385-393 (2006)
10. Calinon, S., Billard, A.: A probabilistic programming by demonstration framework handling constraints in joint space and task space. In: International Conference on Intelligent Robots and Systems [3], pp. 367-372
11. Chernova, S., Veloso, M.: Learning equivalent action choices from demonstration. In: International Conference on Intelligent Robots and Systems [3], pp. 1216-1221
12. Chernova, S., Veloso, M.: Interactive policy learning through confidence-based autonomy. Journal of Artifical Intelligence Research 34(1), 1-25 (2009)
13. Colombetti, M., Dorigo, M.: Training agents to perform sequential behavior. Adaptive Behavior 2(3), 247-275 (1994)
14. Crandall, J.W., Goodrich, M.A.: Experiments in adjustable autonomy. In: International Conference on Systems, Man, and Cybernetics, Tuscan, AZ, October 2001, pp. 1624- 1629 (2001)
15. Csat́o, L.: Gaussian Processes - Iterative Sparse Approximations. PhD thesis, Aston University (March 2002)
16. Csat́o, L., Opper, M.: Sparse Online Gaussian Processes. Neural Computation 14(3), 641-669 (2002)
17. Dasgupta, S., Hsu, D., Monteleoni, C.: A general agnostic active learning algorithm. In: Neural Information Processing Systems [2], pp. 353-360
18. Dillmann, R., Rogalla, O., Ehrenmann,M., Zöllner, R.D., Bordegoni, M.: Learning robot behaviour and skills based on human demonstration and advice: the machine learning paradigm. In: International Symposium of Robotics Research, Snowbird, Utah, USA (October 1999)
19. Fox, E.B., Sudderth, E.B., Jordan, M.I., Willsky, A.S.: An HDP-HMM for systems with state persistence. In: International Conference on Machine Learning, Helsinki, Finland, July 2008, pp. 312-319 (2008)
20. Grimes, D.B., Chalodhorn, R., Rao, R.P.N.: Dynamic imitation in a humanoid robot through nonparametric probabilistic inference. In: Robotics: Science and Systems, Philidelphia, PA (August 2006)
21. Grollman, D.H., Jenkins, O.C.: Dogged learning for robots. In: International Conference on Robotics and Automation, Rome, Italy, April 2007, pp. 2483-2488 (2007)
22. Grollman, D.H., Jenkins, O.C.: Learning robot soccer skills from demonstration. In: International Conference on Development and Learning, London, UK, July 2007, pp. 276- 281 (2007)
23. Grollman, D.H., Jenkins, O.C., Wood, F.: Discovering natural kinds of robot sensory experiences in unstructured environments. Journal of Field Robotics 23(11-12), 1077-1089 (2006)
24. Hayes, G., Demiris, J.: A robot controller using learning by imitation. In: International Symposium on Intelligent Robotic Systems, Grenoble, France (July 1994)
25. Inamura, T., Inaba, M., Inoue, H.: Acquisition of probabilistic behavior decision model based on the interactive teaching method. In: International Conference on Advanced Robotics, Tokyo, Japan, October 1999, pp. 523-528 (1999)
26. Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Computation 3(1), 79-87 (1991)
27. Jenkins, O.C., Gonzalez, G., Loper,M.M.: Interactive human pose and action recognition using dynamical motion primitives. International Journal of Humanoid Robotics 4(2), 365-385 (2007)
28. Kober, J., Mohler, B., Peters, J.: Learning perceptual coupling for motor primitives. In: International Conference on Intelligent Robots and Systems [3], pp. 834-839
29. Kohl, N., Stone, P.: Machine learning for fast quadrupedal locomotion. In: National Conference on Artificial Intelligence, pp. 611-616 (2004)
30. Konidaris, G.D., Barto, A.G.: Skill discovery in continuous reinforcement learning domains using skill chaining. Technical Report UM-CS-2008-2024, University of Massachusetts Amherst (2008)
31. Lopes, M., Melo, F., Montesano, L., Santos-Victor, J.: Abstraction Levels for Robotic Imitation: Overview and Computational Approaches. In: Sigaud, O., Peters, J. (eds.) From Motor Learning to Interaction Learning in Robots. SCI, vol.264, pp. 313-355. Springer, Heidelberg (2010)
32. Mackay, D.J.C.: Introduction to Gaussian Processes. In: Mackay, D.J.C. (ed.) Neural Networks and Machine Learning. Springer, Heidelberg (1998)
33. Mccallum, A.K.: Reinforcement learning with selective perception and hidden state. PhD thesis, The University of Rochester (May 1996)
34. Meeds, E., Osindero, S.: An alternative infinite mixture of Gaussian process experts. In: Neural Information Processing Systems, Vancouver, CAN, December 2005, pp. 883-890 (2005)
35. Ng, A.Y., Russell, S.: Algorithms for inverse reinforcement learning. In: International Conference on Machine Learning, Stanford, CA, June 2000, pp. 663-670 (2000)
36. Nguyen-Tuong, D., Peters, J., Seeger, M.: Computed torque control with nonparametric regression models. In: Proceedings of the 2008 American Control Conference, ACC 2008 (2008)
37. Nguyen-Tuong, D., Seeger, M., Peters, J.: Real-time Local GP Model Learning. In: Sigaud, O., Peters, J. (eds.) From Motor Learning to Interaction Learning in Robots. SCI, vol.264, pp. 193-207. Springer, Heidelberg (2010)
38. Nicolescu, M., Mataríc, M.J.: Natural methods for robot task learning: Instructive demonstration, generalization and practice. In: International Joint Conference on Autonomous Agents and Multi-Agent Systems, Melbourne, AUS, July 2003, pp. 241-248 (2003)
39. Nicolescu, M.N., Jenkins, O.C., Stanhope, A.: Fusing robot behaviors for human-level tasks. In: International Conference on Development and Learning, London, UK, July 2007, pp. 76-81 (2007)
40. Nolfi, S., Tani, J.: Extracting regularities in space and time through a cascade of prediction networks: The case of a mobile robot navigating in a structured environment. Connection Science 11(2), 129-152 (1999)
41. Platt Jr., R., Fagg, A.H., Grupen, R.A.: Manipulation gaits: sequences of grasp control tasks. In: International Conference on Robotics and Automation, April - May 2004, pp. 801-806 (2004)
42. Porta, J.M., Vlassis, N., Spaan, M.T.J., Poupart, P.: Point-based value iteration for continuous pomdps. Journal of Machine Learning Research 7(11), 2329-2367 (2006)
43. Quinonero-Candela, J., Rasmussen, C.E.: A unifying view of sparse approximate gaussian process regression. Journal ofMachine Learning Research 6(12), 1939-1959 (2005)
44. Rasmussen, C.,Ghahramani, Z.: Infinite mixtures of Gaussian process experts. In: Neural Information Processing Systems, Vancouver, CAN, December 2001, pp. 881-888 (2001)
45. Rybski, P.E., Yoon, K., Stolarz, J., Veloso, M.: Interactive robot task training through dialog and demonstration. In: International Conference on Human-Robot Interaction, Arlington, VA, March 2007, pp. 255-262 (2007)
46. Schaal, S., Ijspeert, A., Billard, A.: Computational approaches to motor learning by imitation. Philosophical Transaction of the Royal Society of London 358(1431), 537-547 (2003)
47. Smart, W.D., Kaelbling, L.P.: Effective reinforcement learning for mobile robots. In: International Conference on Robotics and Automation, Washington, D.C., May 2002, pp. 3404-3410 (2002)
48. Snelson, E., Ghahramani, Z.: Sparse gaussian processes using pseudo-inputs. In: Neural Information Processing Systems [1], pp. 1257-1264
49. Stone, P., Veloso, M.: Beating a defender in robotic soccer: Memory-based learning of a continuous function. In: Neural Information Processing Systems, Vancouver, CAN, December 1996, pp. 896-902 (1996)
50. Stone, P., Veloso, M.M.: Layered learning. In: European Conference on Machine Learning, Barcelona, Catalonia, Spain, May 2000, pp. 369-381 (2000)
51. Tani, J., Nolfi, S.: Learning to perceive the world as articulated: An approach for hierarchical learning in sensory-motor systems. Neural Networks 12(7-8), 1131-1141 (1999)
52. Thomaz, A.L., Breazeal, C.: Transparency and socially guided machine learning. In: International Conference on Development and Learning, Bloomington, IN, May 2006, pp. 3475-3480 (2006)
53. Trafton, J.G., Schultz, A.C., Bugajska, M., Mintz, F.: Perspective-taking with robots: experiments and models. In: International Symposium on Robot & Human Interaction, Nashville, TN, August 2005, pp. 580-584 (2005)
54. Vijayakumar, S., D'Souza, A., Schaal, S.: Incremental online learning in high dimensions. Neural Computation 17(12), 2602-2634 (2005)
55. Wood, F., Grollman, D.H., Heller, K.A., Jenkins, O.C., Black,M.: Incremental Nonparametric Bayesian Regression. Technical Report CS-08-107, Brown University Department of Computer Science (2008)
56. Wu, X., Kofman, J.: Human-inspired robot task learning from human teaching. In: International Conference on Robotics and Automation, Pasadena, CA, May 2008, pp. 3334- 3339 (2008)