R. Rahmatizadeh, P. Abolghasemi, A. Behal, and L. Bölöni

Learning real manipulation tasks from virtual demonstrations using LSTM and MDN


Cite as:

R. Rahmatizadeh, P. Abolghasemi, A. Behal, and L. Bölöni. Learning real manipulation tasks from virtual demonstrations using LSTM and MDN. In Proc. of Thirty-Second AAAI Conf. on Artificial Intelligence (AAAI-2018), February 2018.

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Abstract:

Robots assisting the disabled or elderly must perform complex manipulation tasks and must adapt to the home environment and preferences of their user. Learning from demonstration is a promising choice, that would allow the non-technical user to teach the robot different tasks. However, collecting demonstrations in the home environment of a disabled user is time consuming, disruptive to the comfort of the user, and present safety challenges. It would be desirable to perform the demonstrations in a virtual environment. In this paper we describe a solution to the challenging problem of behavior transfer from virtual demonstration to a physical robot. The virtual demonstrations are used to train a deep neural network based controller, which is using a Long Short Term Memory (LSTM) recurrent neural network to generate trajectories. The training process uses a Mixture Density Network (MDN) to calculate an error signal suitable for the multimodal nature of demonstrations. The controller learned in the virtual environment is transferred to a physical robot (a Rethink Robotics Baxter). An off-the-shelf vision component is used to substitute for geometric knowledge available in the simulation and an inverse kinematics module is used to allow the Baxter to enact the trajectory. Our experimental studies validate the three contributions of the paper: (1) the controller learned from virtual demonstrations can be used to successfully perform the manipulation tasks on a physical robot and (2) the LSTM+MDN architectural choice outperforms other choices, such as the use of feedforward networks and mean-squared error based training signals and (3) allowing imperfect demonstrations in the training set also allow the controller to learn how to correct its manipulation mistakes.

BibTeX:

@inproceedings{Rahmatizadeh-2018-RealVirtual,
  author    = {R. Rahmatizadeh and
               P. Abolghasemi and
               A. Behal and
               L. B{\"{o}}l{\"{o}}ni},
  title     = {Learning real manipulation tasks from virtual demonstrations using LSTM and MDN},
  booktitle={Proc. of Thirty-Second AAAI Conf. on Artificial Intelligence (AAAI-2018)},
  year = "2018",
  month = "February",
  location = "New Orleans, Lousiana, USA",
  abstract = {
    Robots assisting the disabled or elderly must perform complex manipulation tasks and must adapt to the home environment and preferences of their user. Learning from demonstration is a promising choice, that would allow the non-technical user to teach the robot different tasks. However, collecting demonstrations in the home environment of a disabled user is time consuming, disruptive to the comfort of the user, and present safety challenges. It would be desirable to perform the demonstrations in a virtual environment.
    In this paper we describe a solution to the challenging problem of behavior transfer from virtual demonstration to a physical robot. The virtual demonstrations are used to train a deep neural network based controller, which is using a Long Short Term Memory (LSTM) recurrent neural network to generate trajectories. The training process uses a Mixture Density Network (MDN) to calculate an error signal suitable for the multimodal nature of demonstrations. The controller learned in the virtual environment is transferred to a physical robot (a Rethink Robotics Baxter). An off-the-shelf vision component is used to substitute for geometric knowledge available in the simulation and an inverse kinematics module is used to allow the Baxter to enact the trajectory.
    Our experimental studies validate the three contributions of the paper: (1) the controller learned from virtual demonstrations can be used to successfully perform the manipulation tasks on a physical robot and (2) the LSTM+MDN architectural choice outperforms other choices, such as the use of feedforward networks and mean-squared error based training signals and (3) allowing imperfect demonstrations in the training set also allow the controller to learn how to correct its manipulation mistakes.
  }
}

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