Sromona Chatterjee

Group(s): Neural Control and Robotics
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    Chatterjee, S. and Nachstedt, T. and Wörgötter, F. and Tamosiunaite, M. and Manoonpong, P. and Enomoto, Y. and Ariizumi, R. and Matsuno, F. (2014).
    Reinforcement learning approach to generate goal-directed locomotion of a snake-like robot with screw-drive units. 23rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD), 1-7. DOI: 10.1109/RAAD.2014.7002234.
    BibTeX:
    @inproceedings{chatterjeenachstedtwoergoetter2014,
      author = {Chatterjee, S. and Nachstedt, T. and Wörgötter, F. and Tamosiunaite, M. and Manoonpong, P. and Enomoto, Y. and Ariizumi, R. and Matsuno, F.},
      title = {Reinforcement learning approach to generate goal-directed locomotion of a snake-like robot with screw-drive units},
      pages = {1-7},
      booktitle = {23rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD)},
      year = {2014},
      month = {Sept},
      doi = {10.1109/RAAD.2014.7002234},
      abstract = {In this paper we apply a policy improvement algorithm called Policy Improvement with Path Integrals (PI2) to generate goal-directed locomotion of a complex snake-like robot with screw-drive units. PI2 is numerically simple and has an ability to deal with high dimensional systems. Here, this approach is used to find proper locomotion control parameters, like joint angles and screw-drive velocities, of the robot. The learning process was achieved using a simulated robot and the learned parameters were successfully transferred to the real one. As a result the robot can locomote toward a given goal.}}
    Abstract: In this paper we apply a policy improvement algorithm called Policy Improvement with Path Integrals (PI2) to generate goal-directed locomotion of a complex snake-like robot with screw-drive units. PI2 is numerically simple and has an ability to deal with high dimensional systems. Here, this approach is used to find proper locomotion control parameters, like joint angles and screw-drive velocities, of the robot. The learning process was achieved using a simulated robot and the learned parameters were successfully transferred to the real one. As a result the robot can locomote toward a given goal.
    Review:
    Chatterjee, S. and Nachstedt, T. and Tamosiunaite, M. and Wörgötter, F. and Enomoto, Y. and Ariizumi, R. and Matsuno, F. and Manoonpong, P. (2015).
    Learning and Chaining of Motor Primitives for Goal-Directed Locomotion of a Snake-Like Robot with Screw-Drive Units. International Journal of Advanced Robotic Systems, 12, 12. DOI: 10.5772/61621.
    BibTeX:
    @article{chatterjeenachstedttamosiunaite2015,
      author = {Chatterjee, S. and Nachstedt, T. and Tamosiunaite, M. and Wörgötter, F. and Enomoto, Y. and Ariizumi, R. and Matsuno, F. and Manoonpong, P.},
      title = {Learning and Chaining of Motor Primitives for Goal-Directed Locomotion of a Snake-Like Robot with Screw-Drive Units},
      journal = {International Journal of Advanced Robotic Systems},
      year = {2015},
      volume= {12},
      number = {12},
      doi = {10.5772/61621},
      abstract = {In this paper we apply a policy improvement algorithm called Policy Improvement with Path Integrals (PItextlesssuptextgreater2textless/suptextgreater) to generate goal-directed locomotion of a complex snake-like robot with screw-drive units. PItextlesssuptextgreater2textless/suptextgreater is numerically simple and has an ability to deal with high dimensional systems. Here, this approach is used to find proper locomotion control parameters, like joint angles and screw-drive velocities, of the robot. The learning process was achieved using a simulated robot and the learned parameters were successfully transferred to the real one. As a result the robot can locomote toward a given goal. © 2014 IEEE.}}
    Abstract: In this paper we apply a policy improvement algorithm called Policy Improvement with Path Integrals (PItextlesssuptextgreater2textless/suptextgreater) to generate goal-directed locomotion of a complex snake-like robot with screw-drive units. PItextlesssuptextgreater2textless/suptextgreater is numerically simple and has an ability to deal with high dimensional systems. Here, this approach is used to find proper locomotion control parameters, like joint angles and screw-drive velocities, of the robot. The learning process was achieved using a simulated robot and the learned parameters were successfully transferred to the real one. As a result the robot can locomote toward a given goal. © 2014 IEEE.
    Review:

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