La recerca s'articula en dues línies amb els objectius que es detallen a continuació.

Percepció i Manipulació:
1. Enllaçar percepció i acció utilitzant mètodes geomètrics i estadístics per al modelatge de l'entorn i del propi robot, per a la planificació de tasques i moviments, i per a l'aprenentatge.
2. Aprofundir en l'aprenentatge per reforçament i en l'aprenentatge per demostració, en particular el "mestratge", com a base per a la interacció entre robots, humans i l'entorn.

Cinemàtica i Disseny de Robots:
3. Trobar mètodes generals i complets per a l'anàlisi i la planificació de moviments lliures de col·lisió de mecanismes.
4. Desenvolupar noves estructures mecàniques, preferentment robots paral·lels i robots basats en estructures "tensegrity".
5. Incrementar i millorar l'expertesa del grup en l'àrea del disseny mecànic

http://futur.upc.edu/ROBiri


La investigación se articula en dos líneas con los objectivos que se detallan a continuación.

Percepción y Manipulación:
1. Enlazar percepción y acción utilitzando métodos geométricos y estadísticos para el modelado del entorno y del propio robot, para la planificación de tareas y movimientos, y para el aprendizaje.
2. Profundizar en el aprendizaje por refuerzo y en el aprendizaje por demostración, en particular el entrenamiento, como base para la interacción entre robots, humanos y el entorno.

Cinemática y Diseño de Robots:
3. Encontrar métodos generales y completos para análisis y planificación de movimientos libres de colisión.
4. Desarrollar nuevas estructuras mecánicas, preferentmente robots paralelos y robots "tensegrity".
5. Incrementar y mejorar la competencia del grupo en el área del diseño mecánico.

http://futur.upc.edu/ROBiri


Research is organized in two lines with the following goals.

Perception and Manipulation:
1. Linking perception and action using geometric and statistic methods for modelling the environment and the robot, for task and motion planning, and for learning.
2. Deepening on reinforcement learning and in learning by demonstration, in particular "coaching", as a basis for the interaction of robots, humans and the environtment.

Kinematics and Robot Design:
3. Finding general and complete methods for the analysis of mechanisms and for planning collision-free motions.
4. Developing new mechanical structures, especially parallel robots and robots based on tensegrity structures.
5. Increasing and enhancing the expertise of the group in the area of mechanical design.

http://futur.upc.edu/ROBiri


Research is organized in two lines with the following goals.

Perception and Manipulation:
1. Linking perception and action using geometric and statistic methods for modelling the environment and the robot, for task and motion planning, and for learning.
2. Deepening on reinforcement learning and in learning by demonstration, in particular "coaching", as a basis for the interaction of robots, humans and the environtment.

Kinematics and Robot Design:
3. Finding general and complete methods for the analysis of mechanisms and for planning collision-free motions.
4. Developing new mechanical structures, especially parallel robots and robots based on tensegrity structures.
5. Increasing and enhancing the expertise of the group in the area of mechanical design.

http://futur.upc.edu/ROBiri

Enviaments recents

  • Grasping novel objects 

    Covallero, Nicola; Alenyà Ribas, Guillem (2016)
    Report de recerca
    Accés obert
    The work explained in this technical report is about evaluating some recent algorithms to grasp unforeseen objects for table clearance tasks. A tabletop object segmentation algorithm is proposed, and two recently published ...
  • Action recognition based on efficient deep feature learning in the spatio-temporal domain 

    Husain, Syed Farzad; Dellen, Babette Karla Margarete; Torras, Carme (Institute of Electrical and Electronics Engineers (IEEE), 2016)
    Article
    Accés obert
    Hand-crafted feature functions are usually designed based on the domain knowledge of a presumably controlled environment and often fail to generalize, as the statistics of real-world data cannot always be modeled correctly. ...
  • Learning relational dynamics of stochastic domains for planning 

    Martínez Martínez, David; Alenyà Ribas, Guillem; Torras, Carme; Ribeiro, Tony; Inoue, Katsumi (2016)
    Text en actes de congrés
    Accés obert
    Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. However, they rely on a model of the domain, which may be costly to either hand code or automatically learn for complex ...
  • Learning physical collaborative robot behaviors from human demonstrations 

    Rozo Castañeda, Leonel; Calinon, Sylvain; Caldwell, Darwin; Jimenez Schlegl, Pablo; Torras, Carme (Institute of Electrical and Electronics Engineers (IEEE), 2016-04-01)
    Article
    Accés obert
    Robots are becoming safe and smart enough to work alongside people not only on manufacturing production lines, but also in spaces such as houses, museums, or hospitals. This can be significantly exploited in situations in ...
  • Structured learning of assignment models for neuron reconstruction to minimize topological errors 

    Funke, Jan; Klein, J.; Moreno-Noguer, Francesc; Cardona, A.; Cook, M. (2016)
    Text en actes de congrés
    Accés obert
    Structured learning provides a powerful framework for empirical risk minimization on the predictions of structured models. It allows end-to-end learning of model parameters to minimize an application specific loss function. ...
  • Interactive multiple object learning with scanty human supervision 

    Villamizar Vergel, Michael Alejandro; Garrell Zulueta, Anais; Sanfeliu Cortés, Alberto; Moreno-Noguer, Francesc (2016-08)
    Article
    Accés obert
    We present a fast and online human-robot interaction approach that progressively learns multiple object classifiers using scanty human supervision. Given an input video stream recorded during the human robot interaction, ...
  • Execution fault recovery in robot programming by demonstration using multiple models 

    Hoyos, Jose; Prieto, Flavio; Alenyà Ribas, Guillem; Torras, Carme (2016)
    Article
    Accés obert
    Deformable object (e.g., clothes) manipulation by a robot in interaction with a human being presents several interesting challenges. Due to texture and deformability, the object can get hooked in the human limbs. Moreover, ...
  • Combining semantic and geometric features for object class segmentation of indoor scenes 

    Husain, Syed Farzad; Schulz, Hannes; Dellen, Babette Karla Margarete; Torras, Carme; Behnke, Sven (Institute of Electrical and Electronics Engineers (IEEE), 2016)
    Article
    Accés obert
    Scene understanding is a necessary prerequisite for robots acting autonomously in complex environments. Low-cost RGB-D cameras such as Microsoft Kinect enabled new methods for analyzing indoor scenes and are now ubiquitously ...
  • Sequential non-rigid structure from motion using physical priors 

    Agudo Martínez, Antonio; Moreno-Noguer, Francesc; Calvo, Begoña; Martinez Montiel, José Maria (2016-05-01)
    Article
    Accés obert
    We propose a new approach to simultaneously recover camera pose and 3D shape of non-rigid and potentially extensible surfaces from a monocular image sequence. For this purpose, we make use of the Extended Kalman Filter ...
  • Incremental learning of skills in a task-parameterized Gaussian Mixture Model 

    Hoyos, Jose; Prieto, Flavio; Alenyà Ribas, Guillem; Torras, Carme (2016)
    Article
    Accés obert
    Programming by demonstration techniques facilitate the programming of robots. Some of them allow the generalization of tasks through parameters, although they require new training when trajectories different from the ones ...

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