A robot learning from demonstration framework to perform force-based manipulation tasks

Intelligent Service Robotics

Volumn 6, Issue 1, 2013, Pages 33-51

  Rozo, Leonel ^a   Jiménez, Pablo ^a   Torras, Carme ^a  

^a CSIC   (Spain)

Author keywords

GMR; Haptic perception; HMM; Imitation learning; Mutual information; Programming by demonstration; Robotic manipulation

Indexed keywords

AGRICULTURAL ROBOTS; DEMONSTRATIONS; HIDDEN MARKOV MODELS; ROBOT PROGRAMMING; SOCIAL ROBOTS; TRELLIS CODES;

HAPTIC PERCEPTION; IMITATION LEARNING; MUTUAL INFORMATIONS; PROGRAMMING BY DEMON-STRATION; ROBOTIC MANIPULATION;

EDUCATIONAL ROBOTS;

EID: 84872333655     PISSN: 18612776     EISSN: 18612784     Source Type: Journal    
DOI: 10.1007/s11370-012-0128-9     Document Type: Article

References (50)

1. Billard A, Calinon S, Dillmann R, Schaal S (2008) Springer handbook of robotics, chapter 59. Robot programming by demonstration. Springer, Berlin, pp 1371-1394.
2. Argall B, Chernova S, Veloso M, Browning B (2009) A survey of robot learning by demonstration. Robot Auton Syst 57(5): 469-483.
3. Goodrich M, Schultz A (2007) Human-robot interaction: a survey. Found Trends Hum-Comput Interact 1(3): 203-275.
4. Najmaei N, Kermani M (2011) Applications of artificial intelligence in safe human-robot interactions. Trans Syst Man Cybern B 41(2): 448-459.
5. Dillmann R (2004) Teaching and learning of robot tasks via observation of human performance. Robot Auton Syst 47(2-3): 109-116.
6. Riley M, Ude A, Atkeson C, Cheng G (2006) Coaching: an approach to efficiently and intuitively create humanoid robot behaviors. In: International conference on humanoid robots, pp 567-574.
7. Bentivegna D, Atkeson C, Cheng G (2004) Learning tasks from observation and practice. Robot Auton Syst 47(2-3): 163-169.
8. Grollman D, Jenkins O (2007) Dogged learning for robots. In: International conference on robotics and automation. pp 2483-2488.
9. 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.
10. Inamura T, Kojo N, Sonoda T, Sakamoto K, Okada K, Inaba M (2005) Intent imitation using wearable motion capturing system with on-line teaching of task attention. In: International conference on humanoid robots. pp 469-474.
11. Calinon S, Billard A (2007) Incremental learning of gestures by imitation in a humanoid robot. In: International conference on humanoids robots. pp 255-262.
12. Evrard P, Gribovskaya E, Calinon S, Billard A, Khedda A (2009) Teaching physical collaborative tasks: object-lifting case study with a humanoid. In: International conference on humanoids robots. pp 399-404.
13. Gribovskaya E, Kheddar A, Billard A (2011) Motion learning and adaptive impedance for robot control during physical interaction with humans. In: International conference on robotics and automation. pp 4326-4332.
14. Kormushev P, Calinon S, Caldwell D (2011) Imitation learning of positional and force skills demonstrated via kinesthetic teaching and haptic input. Adv Robot 25(5): 581-603.
15. Grollman D, Jenkins O (2010) From motor to interaction learning in robots, chapter can we learn finite state machine robot controllers from interactive demonstration? Springer, Berlin, pp 407-430.
16. Cabras S, Castellanos M, Staffetti E (2010) Contact-state classification in human-demonstrated robot compliant motion tasks using the boosting algorithm. Trans Syst Man Cybern B 40(5): 1372-1386.
17. Schaal S, Ijspeert A, Billard A (2005) Computational approaches to motor learning by imitation. Phil Trans R Soc Lond B Biol Sci 358(1431): 537-547.
18. Kulić D, Takano W, Nakamura Y (2008) Incremental learning, clustering and hierarchy formation of whole body motion patterns using adaptive hidden Markov chains. Int J Robot Res 27(7): 761-784.
19. Atkeson C, Schaal S (1997) Robot learning by demonstration. In: International conference on machine learning. pp 12-20.
20. Ijspeert A, Nakanishi J, Schaal S (2001) Trajectory formation for imitation with nonlinear dynamical systems. In: International conference on intelligent robots and systems. pp 752-757.
21. Cederborg T, Li M, Baranes A, Oudeyer P (2010) Incremental local online gaussian mixture regression for imitation learning of multiple tasks. In: International conference on intelligent robots and systems. pp 267-274.
22. Billard A, Epars Y, Calinon S, Cheng G, Schaal S (2004) Discovering optimal imitation strategies. Robot Auton Syst 47(2-3): 69-77.
23. Economou D, Lee C, Mavroidis C, Antoniadis I (2000) Robust vibration suppression in flexible payloads carried by robot manipulators using digital filtering of joint trajectories. In: International symposium on robotics and automation. pp 244-249.
24. Dines K (1977) Constrained least squares filtering. Trans Acoust Speech Signal Process 25(4): 346-350.
25. Uchiyama M, Kitagaki K (1989) Dynamic force sensing for high-speed robot manipulation using kalman filtering techniques. In: International conference on decision and control. pp 2147-2152.
26. Garcia J, Robertsson A, Ortega J, Johansson R (2006) Generalized contact force estimator for a robot manipulator. In: International conference on robotics and automation. pp 4019-4024.
27. Tamosiunaite M, Nemec B, Ude A, Wörgötter F (2011) Learning to pour with a robot arm combining goal and shape learning for dynamic movement primitives. Robot Auton Syst 59(11): 910-922.
28. Cakmak M, Thomaz A (2012) Designing robot learners that ask good questions. In: International conference on human-robot interaction. pp 17-24.
29. Nehaniv C, Dautenhahn K (2000) Of hummingbirds and helicopters: an algebraic framework for interdisciplinary studies of imitation and its applications. Interdiscip Approach Robot Learn World Sci Ser Robot Intel Syst 24: 136-161.
30. Calinon S, Guenter F, Billard A (2007) On learning, representing, and generalizing a task in a humanoid robot. Trans Syst Man Cybern B 37(2): 286-298.
31. Calinon S, Billard A (2008) A probabilistic programming by demonstration framework handling constraints in joint space and task space. In: International conference on intelligent robots and systems. pp 367-372.
32. Lee D, Ott C (2011) Incremental kinesthetic teaching of motion primitives using the motion refinement tube. Auton Robots 31: 115-131.
33. Torkkola K (2003) Feature extraction by non-parametric mutual information maximization. J Mach Learn Res 3: 1415-1438.
34. Guyon I (2003) An introduction to variable and feature selection. J Mach Learn Res 3: 1157-1182.
35. Battiti R (1994) Using mutual information for selecting features in supervised neural net learning. Trans Neural Netw 5(4): 537-550.
36. Ikeda T, Ishiguro H, Asada M (2003) Adaptive fusion of sensor signals based on mutual information maximization. In: International conference on robotics and automation. pp 4398-4402.
37. Wells G, Torras C (2001) Assessing image features for vision-based robot positioning. J Intell Robot Syst 30(1): 95-118.
38. Shannon C (2001) A mathematical theory of communication. SIGMOBILE Mob Comput Commun Rev 5: 3-55.
39. Kwak N, Choi C (2002) Input feature selection for classification problems. Trans Neural Netw 13(1): 143-159.
40. Estévez P, Tesmer M, Perez C, Zurada J (2009) Normalized mutual information feature selection. Trans Neural Netw 20(2): 189-201.
41. Rozo L, Jiménez P, Torras C (2010) Sharpening haptic inputs for teaching a manipulation skill to a robot. In: International conference on applied bionics and biomechanics. pp 370-377.
42. Rabiner L, Juang B (1993) Fundamentals of speech recognition. Prentice Hall, Englewood Cliffs.
43. Billard A, Calinon S, Guenter F (2006) Discriminative and adaptive imitation in uni-manual and bi-manual tasks. Robot Auton Syst 54: 370-384.
44. Kulić D, Nakamura Y (2010) Incremental learning of human behaviors using hierarchical hidden Markov models. In: International conference on intelligent robots and systems. pp 4649-4655.
45. Dong S, Naghdy F (2007) Application of hidden Markov model to acquisition of manipulation skills from haptic rendered virtual environment. Roboti Comput-Integr Manuf 23(3): 351-360.
46. Rabiner L (1989) A tutorial on hidden Markov models and selected applications in speech recognition. In: Proceedings of the IEEE. pp 257-286.
47. Calinon S, D'halluin F, Sauser E, Caldwell D, Billard A (2010) Learning and reproduction of gestures by imitation. Robot Autom Mag 17(2): 44-54.
48. Calinon S (2009) Robot programming by demonstration: a probabilistic approach. EPFL/CRC Press, Boca Raton.
49. Steinfeld A, Fong T, Kaber D, Lewis M, Scholtz J, Schultz A, Goodrich M (2006) Common metrics for human-robot interaction. In: International conference on human-robot interaction. pp 33-40.
50. Wilson AD, Bobick AF (1999) Parametric hidden Markov models for gesture recognition. Trans Pattern Anal Mach Intell 21(9): 884-900.