Rozo Castañeda, Leonel
Total activity: 8
Institute
Institute of Robotics and Industrial Informatics
E-mail
leonel.rozoestudiant.upc.edu
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Scientific and technological production
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    A robot learning from demonstration framework to perform force-based manipulation tasks  Open access

     Rozo Castañeda, Leonel; Jimenez Schlegl, Pablo; Torras, Carme
    Intelligent Service Robotics
    Vol. 6, num. 1, p. 33-51
    DOI: 10.1007/s11370-012-0128-9
    Date of publication: 2013
    Journal article

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    This paper proposes an end-to-end learning from demonstration framework for teaching force-based manipulation tasks to robots. The strengths of this work are manyfold. First, we deal with the problem of learning through force perceptions exclusively. Second, we propose to exploit haptic feedback both as a means for improving teacher demonstrations and as a human¿robot interaction tool, establishing a bidirectional communication channel between the teacher and the robot, in contrast to the works using kinesthetic teaching. Third, we address the well-known what to imitate? problem from a different point of view, based on the mutual information between perceptions and actions. Lastly, the teacher¿s demonstrations are encoded using a Hidden Markov Model, and the robot execution phase is developed by implementing a modified version of Gaussian Mixture Regression that uses implicit temporal information from the probabilistic model, needed when tackling tasks with ambiguous perceptions. Experimental results show that the robot is able to learn and reproduce two different manipulation tasks, with a performance comparable to the teacher¿s one.

    This paper proposes an end-to-end learning from demonstration framework for teaching force-based manipulation tasks to robots. The strengths of this work are manyfold. First, we deal with the problem of learning through force perceptions exclusively. Second, we propose to exploit haptic feedback both as a means for improving teacher demonstrations and as a human–robot interaction tool, establishing a bidirectional communication channel between the teacher and the robot, in contrast to the works using kinesthetic teaching. Third, we address the well-known what to imitate? problem from a different point of view, based on the mutual information between perceptions and actions. Lastly, the teacher’s demonstrations are encoded using a Hidden Markov Model, and the robot execution phase is developed by implementing a modified version of Gaussian Mixture Regression that uses implicit temporal information from the probabilistic model, needed when tackling tasks with ambiguous perceptions. Experimental results show that the robot is able to learn and reproduce two different manipulation tasks, with a performance comparable to the teacher’s one.

    Postprint (author’s final draft post-refereeing)

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    Dynamically consistent probabilistic model for robot motion learning  Open access

     Pardo Ayala, Diego Esteban; Rozo Castañeda, Leonel; Alenyà Ribas, Guillem; Torras, Carme
    Workshop on Learning and Interaction in Haptic Robots
    p. 1-2
    Presentation's date: 2012
    Presentation of work at congresses

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    This work presents a probabilistic model for learning robot tasks from human demonstrations using kinesthetic teaching. The difference with respect to previous works is that a complete state of the robot is used to obtain a consistent representation of the dynamics of the task. The learning framework is based on hidden Markov models and Gaussian mixture regression, used for coding and reproducing the skills. Benefits of the proposed approach are shown in the execution of a simple self-crossing trajectory by a 7-DoF manipulator.

  • Robot learning from demonstration in the force domain

     Rozo Castañeda, Leonel; Jimenez Schlegl, Pablo; Torras, Carme
    IJCAI Workshop on Agents Learning Interactively from Human Teachers
    p. 1-6
    Presentation's date: 2011
    Presentation of work at congresses

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    Researchers are becoming aware of the importance of other information sources besides visual data in robot learning by demonstration (LbD). Forcebased perceptions are shown to convey very relevant information – missed by visual and position sensors – for learning specific tasks. In this paper, we review some recent works using forces as input data in LbD and Human-Robot interaction (HRI) scenarios, and propose a complete learning framework for teaching force-based manipulation skills to a robot through a haptic device. We suggest to use haptic interfaces not only as a demonstration tool but also as a communication channel between the human and the robot, getting the teacher more involved in the teaching process by experiencing the force signals sensed by the robot. Within the proposed framework, we provide solutions for treating force signals, extracting relevant information about the task, encoding the training data and generalizing to perform successfully under unknown conditions.

    Postprint (author’s final draft)

  • Robot learning from demonstration of force-based tasks with multiple solution trajectories

     Rozo Castañeda, Leonel; Jimenez Schlegl, Pablo; Torras, Carme
    International Conference on Advanced Robotics
    p. 124-129
    DOI: 10.1109/ICAR.2011.6088633
    Presentation's date: 2011
    Presentation of work at congresses

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  • GARNICS: Gardening with a Cognitive System (FP7-ICT-247947)

     Moreno Noguer, Francesc d'Assis; Torras, Carme; Agostini, Alejandro Gabriel; Husain, Syed Farzad; Dellen, Babette Karla Margarete; Alenyà Ribas, Guillem; Jimenez Schlegl, Pablo; Thomas Arroyo, Federico; Rozo Castañeda, Leonel; Foix Salmeron, Sergi
    Competitive project

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  • Learning force-based robot skills from haptic demonstration

     Rozo Castañeda, Leonel; Jimenez Schlegl, Pablo; Torras, Carme
    International Conference of the Catalan Association for Artificial Intelligence
    p. 331-341
    DOI: 10.3233/978-1-60750-643-0-331
    Presentation's date: 2010
    Presentation of work at congresses

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    Sharpening haptic inputs for teaching a manipulation skill to a robot  Open access

     Rozo Castañeda, Leonel; Jimenez Schlegl, Pablo; Torras, Carme
    IEEE International Conference on Applied Bionics and Biomechanics
    p. 331-340
    Presentation's date: 2010
    Presentation of work at congresses

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    Gaussian mixtures-based learning algorithms are suitable strategies for trajectory learning and skill acquisition, in the context of programming by demonstration (PbD). Input streams other than visual information, as used in most applications up to date, reveal themselves as quite useful in trajectory learning experiments where visual sources are not available. In this work we have used force/torque feedback through a haptic device for teaching a teleoperated robot to empty a rigid container. Structure vibrations and container inertia appeared to considerably disrupt the sensing process, so a filtering algorithm had to be devised. Moreover, some input variables seemed much more relevant to the particular task to be learned than others, which lead us to analyze the training data in order to select those relevant features through principal component analysis and a mutual information criterion. Then, a batch version of GMM/GMR [1], [2] was implemented using different training datasets (original, pre-processed data through PCA and MI). Tests where the teacher was instructed to follow a strategy compared to others where he was not lead to useful conclusions that permit devising the new research stages.

    Postprint (author’s final draft)

  • Robot learning of container-emptying skills through haptic demonstration

     Rozo Castañeda, Leonel; Jimenez Schlegl, Pablo; Torras, Carme
    Date: 2009
    Report

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