Research Project

I-DRESS: Assistive interactive robotic system for support in dressing

Type

European Project

Start Date

01/12/2015

End Date

30/11/2018

Project Code

PCIN-2015-147

Project illustration

Staff

Project Description

The main objective of the project is to develop a system that will provide proactive assistance with dressing to disabled users or users such as high-risk health-care workers, whose physical contact with the garments must be limited to avoid contamination. The proposed robotic system consists of two highly dexterous robotic arms, sensors for multi-modal human-robot interaction and safety features.

The system will comprise three major components, each of strong impact to the field of assistive service robotics: (a) intelligent algorithms for user and garment detection and tracking, specifically designed for close and physical human-robot interaction, (b) cognitive functions based on the multi-modal user input, environment modelling and safety, allowing the robot to decide when and how to assist the user, and (c) advanced user interface that facilitates intuitive and safe physical and cognitive interaction for support in dressing. The developed interactive system will be integrated on commercial WAM robotic arms and validated through experimentation with users and human factor analysis in two assistive-dressing scenarios.

Consortium:
Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Spain (Coordinator)
Bristol Robotics Lab, University of West of England, United Kingdom
IDIAP Research Institute, Switzerland

Funding: 740,000 € (European CHIST-ERA 2014 Call*)
*Funds are provided by national research funding organisations, IRI is funded by the Spanish Ministry of Economy and Competitiveness (MINECO).

Project website: https://www.i-dress-project.eu

Project Publications

Journal Publications

  • A. Agudo and F. Moreno-Noguer. Combining local-physical and global-statistical models for sequential deformable shape from motion. International Journal of Computer Vision, 122(2): 371-387, 2017.

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  • E. Simo-Serra, C. Torras and F. Moreno-Noguer. 3D human pose tracking priors using geodesic mixture models. International Journal of Computer Vision, 2017, to appear.

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  • A. Agudo, J.M. Martínez, L. Agapito and B. Calvo. Modal space: A physics-based model for sequential estimation of time-varying shape from monocular video. Journal of Mathematical Imaging and Vision, 57(1): 75–98, 2017.

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  • A. Ramisa, G. Alenyà, F. Moreno-Noguer and C. Torras. A 3D descriptor to detect task-oriented grasping points in clothing. Pattern Recognition, 60: 936-948, 2016.

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  • F. Moreno-Noguer and J.M. Porta. A Bayesian approach to simultaneously recover camera pose and non-rigid shape from monocular images. Image and Vision Computing, 52: 141-153, 2016.

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  • C. Torras. Service robots for citizens of the future. European Review, 24(1): 17-30, 2016.

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  • J.G. Hoyos, F. Prieto, G. Alenyà and C. Torras. Execution Fault Recovery in Robot Programming by Demonstration Using Multiple Models. IEEE Latin America Transactions , 14(2): 517-523, 2016.

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  • A. Agudo, F. Moreno-Noguer, B. Calvo and J.M. Martínez. Real-time 3D reconstruction of non-rigid shapes with a single moving camera. Computer Vision and Image Understanding, 153(12): 37–54, 2016.

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Conference Publications

  • A. Agudo and F. Moreno-Noguer. Global model with local interpretation for dynamic shape reconstruction, 2017 IEEE Winter Conference on Applications of Computer Vision, 2017, Santa Rosa (California), USA, IEEE, to appear.

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  • A. Pumarola, A. Vakhitov, A. Agudo, A. Sanfeliu and F. Moreno-Noguer. PL-SLAM: Real-time monocular visual SLAM with points and lines, 2017 IEEE International Conference on Robotics and Automation, 2017, Singapore, to appear.

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  • A. Agudo and F. Moreno-Noguer. DUST: Dual union of spatio-temporal subspaces for monocular multiple object 3D reconstruction, 2017 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2017, Honolulu, USA, IEEE, to appear.

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  • A. Gabás, E. Corona, G. Alenyà and C. Torras. Robot-aided cloth classification using depth information and CNNs, 9th Conference on Articulated Motion and Deformable Objects, 2016, Palma de Mallorca, Spain, in Articulated Motion of Deformable Objects, Vol 9756 of Lecture Notes in Computer Science, pp. 16-23, 2016, Springer.

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  • A. Agudo and F. Moreno-Noguer. Recovering pose and 3D deformable shape from multi-instance image ensembles, 13th Asian Conference on Computer Vision, 2016, Taipei, Taiwan, in Computer Vision – ACCV 2016, Vol 10114 of Lecture Notes in Computer Science, pp. 291-307, 2017, Springer.

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  • G. Canal, G. Alenyà and C. Torras. Personalization framework for adaptive robotic feeding assistance, 8th International Conference on Social Robotics, 2016, Kansas City, USA, in Social Robotics, Vol 9979 of Lecture Notes in Artificial Intelligence, pp. 22-31, 2016, Springer.

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  • A. Jevtić, A. Colomé, G. Alenyà and C. Torras. User evaluation of an interactive learning framework for single-arm and dual-arm robots, 8th International Conference on Social Robotics, 2016, Kansas City, USA, in Social Robotics, Vol 9979 of Lecture Notes in Artificial Intelligence, pp. 52-61, 2016, Springer.

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  • G. Martín, F. Husain, H. Schulz, S. Frintrop, C. Torras and S. Behnke. Semantic segmentation priors for object discovery, 23rd International Conference on Pattern Recognition, 2016, Cancún, Mexico, to appear.

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  • A. Rubio, L. Yu, E. Simo-Serra and F. Moreno-Noguer. BASS: Boundary-aware superpixel segmentation, 23rd International Conference on Pattern Recognition, 2016, Cancún, Mexico, to appear.

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  • A. Agudo, J.M. Martínez, B. Calvo and F. Moreno-Noguer. Mode-shape interpretation: Re-thinking modal space for recovering deformable shapes, 2016 IEEE Winter Conference on Applications of Computer Vision, 2016, Lake Placid, USA, pp. 1-8, IEEE.

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  • A. Vakhitov, J. Funke and F. Moreno-Noguer. Accurate and linear time pose estimation from points and lines, 14th European Conference on Computer Vision, 2016, Amsterdam, in Computer Vision - ECCV 2016, Vol 9911 of Lecture Notes in Computer Science, pp. 583-599, 2016.

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  • D. Martínez, G. Alenyà, C. Torras, T. Ribeiro and K. Inoue. Learning relational dynamics of stochastic domains for planning, 26th International Conference on Automated Planning and Scheduling, 2016, London, pp. 235-243.

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  • A. Quattoni, A. Ramisa, P. Swaroop, E. Simo-Serra and F. Moreno-Noguer. Structured prediction with output embeddings for semantic image annotation, 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2016, San Diego, pp. 552-557.

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