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Volumn 21, Issue 13, 2007, Pages 1521-1544

Reinforcement learning for imitating constrained reaching movements

Author keywords

DYNAMICAL SYSTEMS; GAUSSIAN MIXTURE MODEL; PROGRAMMING BY DEMONSTRATION; REINFORCEMENT LEARNING

Indexed keywords

ALGORITHMS; COMPUTER PROGRAMMING; DYNAMICAL SYSTEMS; ROBOTS;

EID: 34948857495     PISSN: 01691864     EISSN: 15685535     Source Type: Journal    
DOI: 10.1163/156855307782148550     Document Type: Article
Times cited : (129)

References (30)
  • 3
    • 0029539342 scopus 로고
    • Dynamics of behaviour: Theory and application for autonomous robot architecture
    • Schoner, G., Dose, M., and Engels, C., 1995. Dynamics of behaviour:theory and application for autonomous robot architecture. Robotics Autonomous Syst., 16:213–245.
    • (1995) Robotics Autonomous Syst. , vol.16 , pp. 213-245
    • Schoner, G.1    Dose, M.2    Engels, C.3
  • 5
    • 3042617895 scopus 로고    scopus 로고
    • Autonomous reaching and obstacle avoidance with anthropomorphic arm of a robotics assistant using the attractor dynamics approach
    • New Orleans, LA
    • Iossifidis, I., and Schoner, G., 2004. “ Autonomous reaching and obstacle avoidance with anthropomorphic arm of a robotics assistant using the attractor dynamics approach ”. In Proc. IEEE Int. Conf. on Robotics and Automation 4295–4300. New Orleans, LA
    • (2004) Proc. IEEE Int. Conf. on Robotics and Automation , pp. 4295-4300
    • Iossifidis, I.1    Schoner, G.2
  • 6
    • 33845640093 scopus 로고    scopus 로고
    • Programmable central pattern generators: A application to biped locomotion control
    • Orlando, FL
    • Righetti, L., and Ijspeert, A., 2006. “ Programmable central pattern generators:a application to biped locomotion control ”. In Proc. IEEE Int. Conf. on Robotics and Automation 1585–1590. Orlando, FL
    • (2006) Proc. IEEE Int. Conf. on Robotics and Automation , pp. 1585-1590
    • Righetti, L.1    Ijspeert, A.2
  • 9
    • 0035979437 scopus 로고    scopus 로고
    • Acquisition of stand-up behavior by a real robot using hierarchical reinforcement learning
    • Morimoto, J., and Doya, K., 2001. Acquisition of stand-up behavior by a real robot using hierarchical reinforcement learning. Robotics Autonomous Syst., 36:37–51.
    • (2001) Robotics Autonomous Syst. , vol.36 , pp. 37-51
    • Morimoto, J.1    Doya, K.2
  • 11
    • 85150714688 scopus 로고
    • Reinforcement learning methods for continuous-time Markov decision problems
    • Denver
    • Bratke, S. J., and Duff, M. O., 1994. “ Reinforcement learning methods for continuous-time Markov decision problems ”. In Proc. Neural Information Processing Systems Conf 393–400. Denver
    • (1994) Proc. Neural Information Processing Systems Conf , pp. 393-400
    • Bratke, S.J.1    Duff, M.O.2
  • 12
    • 0033629916 scopus 로고    scopus 로고
    • Reinforcement learning in continuous time and space
    • Doya, K., 2000. Reinforcement learning in continuous time and space. Neural Comput., 12:219–245.
    • (2000) Neural Comput. , vol.12 , pp. 219-245
    • Doya, K.1
  • 17
    • 84873015924 scopus 로고    scopus 로고
    • Least-squares policy evaluation algorithms with linear function approximation
    • Cambridge
    • Nedic, A., and Bertsekas, D., 2001. “ Least-squares policy evaluation algorithms with linear function approximation ”. In LIDS Report LIDS-P-2537, Dec. 2001 Cambridge
    • (2001) LIDS Report LIDS-P-2537, Dec. 2001
    • Nedic, A.1    Bertsekas, D.2
  • 18
    • 0000337576 scopus 로고
    • Simple statistical gradient-following algorithms for connectionist reinforcement learning
    • Williams, R., 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learn., 8:229–256.
    • (1992) Machine Learn. , vol.8 , pp. 229-256
    • Williams, R.1
  • 21
    • 0000396062 scopus 로고    scopus 로고
    • Natural gradient works efficiently in learning
    • Amari, S., 2000. Natural gradient works efficiently in learning. Neural Comput., 10:251–276.
    • (2000) Neural Comput. , vol.10 , pp. 251-276
    • Amari, S.1
  • 22
    • 33749243349 scopus 로고    scopus 로고
    • Autonomous shaping: Knowledge transfer in reinforcement learning
    • Pittsburgh, PA
    • Konidaris, G., and Barto, A., 2006. “ Autonomous shaping:knowledge transfer in reinforcement learning ”. In Proc. Int. Conf. on Machine Learning 497–504. Pittsburgh, PA
    • (2006) Proc. Int. Conf. on Machine Learning , pp. 497-504
    • Konidaris, G.1    Barto, A.2
  • 23
    • 33749242451 scopus 로고    scopus 로고
    • Using inaccurate models in reinforcement learning
    • Pittsburgh, PA
    • Abbeel, P., and Quigley, M., 2006. “ Using inaccurate models in reinforcement learning ”. In Proc. Int. Conf. on Machine Learning 9–16. Pittsburgh, PA
    • (2006) Proc. Int. Conf. on Machine Learning , pp. 9-16
    • Abbeel, P.1    Quigley, M.2
  • 24
    • 33749261645 scopus 로고    scopus 로고
    • An intrinsic reward mechanism for efficient exploration
    • Pittsburgh, PA
    • Simsek, O., and Barto, A., 2006. “ An intrinsic reward mechanism for efficient exploration ”. In Proc. Int. Conf. on Machine Learning 841–848. Pittsburgh, PA
    • (2006) Proc. Int. Conf. on Machine Learning , pp. 841-848
    • Simsek, O.1    Barto, A.2
  • 28
    • 33646162402 scopus 로고    scopus 로고
    • Discriminative and adaptative imitation in uni-manual and bi-manual tasks
    • Billard, A., Calinon, S., and Guenter, F., 2006. Discriminative and adaptative imitation in uni-manual and bi-manual tasks. Robotics and Autonomous Syst., 54:370–384.
    • (2006) Robotics and Autonomous Syst. , vol.54 , pp. 370-384
    • Billard, A.1    Calinon, S.2    Guenter, F.3
  • 29
    • 0036832950 scopus 로고    scopus 로고
    • Technical update: Least-squares temporal difference learning
    • Boyan, J. A., 2002. Technical update:least-squares temporal difference learning. Machine Learn., 49:233–246.
    • (2002) Machine Learn. , vol.49 , pp. 233-246
    • Boyan, J.A.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.