Imitation and Social Learning in Robots, Humans and Animals Behavioural, Social and Communicative Dimensions
Book contents
- Frontmatter
- Contents
- List of plates
- List of figures
- List of tables
- List of contributors
- Introduction: the constructive interdisciplinary viewpoint for understanding mechanisms and models of imitation and social learning
- Part I Correspondence problems and mechanisms
- 1 Imitation: thoughts about theories
- 2 Nine billion correspondence problems
- 3 Challenges and issues faced in building a framework for conducting research in learning from observation
- Part II Mirroring and ‘mind-reading’
- Part III What to imitate?
- Part IV Development and embodiment
- Part V Synchrony and turn-taking as communicative mechanisms
- Part VI Why imitate? – Motivations
- Part VII Social feedback
- Part VIII The ecological context
- Index
- Plate section
- References
3 - Challenges and issues faced in building a framework for conducting research in learning from observation
Published online by Cambridge University Press: 10 December 2009
Book contents
- Frontmatter
- Contents
- List of plates
- List of figures
- List of tables
- List of contributors
- Introduction: the constructive interdisciplinary viewpoint for understanding mechanisms and models of imitation and social learning
- Part I Correspondence problems and mechanisms
- 1 Imitation: thoughts about theories
- 2 Nine billion correspondence problems
- 3 Challenges and issues faced in building a framework for conducting research in learning from observation
- Part II Mirroring and ‘mind-reading’
- Part III What to imitate?
- Part IV Development and embodiment
- Part V Synchrony and turn-taking as communicative mechanisms
- Part VI Why imitate? – Motivations
- Part VII Social feedback
- Part VIII The ecological context
- Index
- Plate section
- References
Summary
Introduction
We are exploring how primitives, small units of behavior, can speed up robot learning and enable robots to learn difficult dynamic tasks in reasonable amounts of time. In this chapter we describe work on learning from observation and learning from practice on air hockey and marble maze tasks. We discuss our research strategy, results, and open issues and challenges.
Primitives are units of behavior above the level of motor or muscle commands. There have been many proposals for such units of behavior in neuroscience, psychology, robotics, artificial intelligence and machine learning (Arkin, 1998; Schmidt, 1988; Schmidt, 1975; Russell and Norvig, 1995; Barto and Mahadevan, 2003). There is a great deal of evidence that biological systems have units of behavior above the level of activating individual motor neurons, and that the organization of the brain reflects those units of behavior (Loeb, 1989). We know that in human eye movement, for example, there are only a few types of movements including saccades, smooth pursuit, vestibular ocular reflex (VOR), optokinetic nystagmus (OKN) and vergence, that general eye movements are generated as sequences of these behavioral units, and that there are distinct brain regions dedicated to generating and controlling each type of eye movement (Carpenter, 1988). We know that there are discrete locomotion patterns, or gaits, for animals with legs (McMahon, 1984). Whether there are corresponding units of behavior for upper limb movement in humans and other primates is not yet clear.
- Type
- Chapter
- Information
- Imitation and Social Learning in Robots, Humans and AnimalsBehavioural, Social and Communicative Dimensions, pp. 47 - 66Publisher: Cambridge University PressPrint publication year: 2007
References
, and (1989). Task-level robot learning: juggling a tennis ball more accurately. In IEEE International Conference on Robotics and Automation, Scottsdale, AZ, 1290–5.Google Scholar
, and (1997). Locally weighted learning. Artificial Intelligence Review, 11, 11–73.CrossRefGoogle Scholar
Balch, T. (1997). Clay: integrating motor schemas and reinforcement learning. Technical Report GIT-CC-97-11, College of Computing, Georgia Institute of Technology, Atlanta, Georgia.
and (2003). Recent advances in hierarchical reinforcement learning. Discrete Event Systems, 13, 41–77.CrossRefGoogle Scholar
(2004). Learning from Observation using Primitives. PhD thesis, Georgia Institute of Technology, Atlanta, GA, USA. http://etd.gatech.edu/theses/available/etd-06202004-213721/.Google Scholar
, , and (2003). Learning from observation and practice at the action generation level. In IEEE-RAS International Conference on Humanoid Robotics (Humanoids 2003), Karlsruhe, Germany.Google Scholar
, , , and (2002). Humanoid robot learning and game playing using PC-based vision. In Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, Switzerland, Vol. 3, 2449–54.Google Scholar
(1986). A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, RA-2(1), 14–23.CrossRefGoogle Scholar
and (1996). Robot programming by human demonstration: adaptation and inconsistency in constrained motion. In IEEE International Conference on Robotics and Automation, Vol. 1, 30–6.Google Scholar
(1998). The MAXQ method for hierarchical reinforcement learning. In Proceedings of the 15th International Conference on Machine Learning. San Francisco, CA: Morgan Kaufmann, 118–26.Google Scholar
and (1988). An exploration of sensorless manipulation. IEEE Journal of Robotics and Automation, 4, 369–79.CrossRefGoogle Scholar
, and (2001). Composable controllers for physics-based character animation. In Proceedings of SIGGRAPH 2001, Los Angeles, CA, 251–60.Google Scholar
, and (2000). Automated derivation of primitives for movement classification. In First IEEE-RAS International Conference on Humanoid Robotics (Humanoids-2000), MIT, Cambridge, MA.Google Scholar
, and (1996). Skill acquisition from human demonstration using a hidden Markov model. In Proceedings of IEEE International Conference on Robotics and Automation, Minneapolis, MN, 2706–11.Google Scholar
and (1996). Building elementary skills from human demonstration. In Proceedings of the IEE International Conference on Robotics and Automation, 2700–5.Google Scholar
, , and (1984). Principles of Neural Science. Norwalle, CT: McGraw-Hill/Appleton and Lange, 4th edn.Google Scholar
and (1993). Toward automatic robot instruction from perception: recognizing a grasp from observation. IEEE International Journal of Robotics and Automation, 9(4), 432–43.Google Scholar
, , and (1994). Learning by watching: extracting reusable task knowledge from visual observation of human performance. IEEE Transactions on Robotics and Automation, 10(6), 799–822.CrossRefGoogle Scholar
and (2001). Spatio-temporal case-based reasoning for behavioral selection. In Proceedings of the 2001 IEEE International Conference on Robotics and Automation, Seoul, Korea, 1627–34.Google Scholar
(1993). Hierarchical learning of robot skills by reinforcement. In Proceedings of the 1993 International Joint Conference on Neural Networks, 181–6.Google Scholar
(1989). The functional organization of muscles, motor units, and tasks. In The Segmental Motor System. New York:Oxford University Press, 22–35.Google Scholar
, , and (1998). Behavior-based primitives for articulated control. In 5th International Conference on Simulation of Adaptive Behavior (SAB-98). Cambridge, MA: MIT Press, 165–70.Google Scholar
and (2001). Automatic discovery of sub-goals in reinforcement learning using diverse density. In Proceedings of the 18th International Conference on Machine Learning. San Francisco, CA: Morgan Kaufmann, 361–68.Google Scholar
, and (2001). Human-like action recognition system on whole body motion-captured file. In Proceedings of the 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, Maui, Hawaii, Vol. 2, 1214–20.Google Scholar
and (1998). Hierarchical reinforcement learning of low-dimensional sub-goals and high-dimensional trajectories. In Proceedings of the 5th International Conference on Neural Information Processing, Vol. 2, 850–3.Google Scholar
, and (1992). The learning of reactive control parameters through genetic algorithms. In Proceedings of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems, Raleigh, NC, 130–7.Google Scholar
and (1995). Artificial Intelligence: A Modern Approach. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
and (2000). Learning to fly: an application of hierarchical reinforcement learning. In Proceedings of the 17th International Conference on Machine Learning. San Francisco, CA: Morgan Kaufmann, 807–14.Google Scholar
Schaal, S. (1997). Learning from demonstration. In Mozer, M. C., Jordan, , and (eds.), Advances in Neural Information Processing Systems, Vol. 9. Cambridge, MA: MIT Press. 1040.
(1975). A schema theory of discrete motor skill learning. Psychological Review, 83, 225–60.CrossRefGoogle Scholar
Spong, M. W. (1999). Robotic air hockey. http://cyclops.csl.uiuc.edu.
and (1995). Automatic learning of assembly tasks using a dataglove system. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 1.Google Scholar
and (2000). Simulating leaping, tumbling, landing and balancing humans. In IEEE International Conference on Robotics and Automation, Vol. 1, 656–62.Google Scholar
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