Geometric Task Networks: Learning Efficient and Explainable Skill Coordination for Object Manipulation

Autor: Mathias Bürger, Meng Guo

Complex manipulation tasks can contain various execution branches of primitive skills in sequence or in parallel under different scenarios. Manual specifications of such branching conditions and associated skill parameters are not only error-prone du

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0aebe0262ae0222bca5a1d5d97c6e2db
http://arxiv.org/abs/2109.08993

Learning to combine primitive skills: A step towards versatile robotic manipulation

Autor: Alexander Pashevich, Cordelia Schmid, Robin Strudel, Josef Sivic, Igor Kalevatykh, Ivan Laptev

Publikováno v: ICRA 2020-IEEE International Conference on Robotics and Automation
ICRA 2020-IEEE International Conference on Robotics and Automation, May 2020, Paris / Virtuel, France. ⟨10.1109/ICRA40945.2020.9196619⟩
ICRA

Manipulation tasks such as preparing a meal or assembling furniture remain highly challenging for robotics and vision. Traditional task and motion planning (TAMP) methods can solve complex tasks but require full state observability and are not adapte

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::51d17447cfd4831aa3b013b88529612f
https://hal.archives-ouvertes.fr/hal-02274969

A Modular Dynamic Sensorimotor Model for Affordances Learning, Sequences Planning, and Tool-Use

Autor: Raphael Braud, Philippe Gaussier, Alexandre Pitti

Publikováno v: IEEE Transactions on Cognitive and Developmental Systems
IEEE Transactions on Cognitive and Developmental Systems, Institute of Electrical and Electronics Engineers, Inc, In press, pp.1-1. ⟨10.1109/TCDS.2016.2647439⟩

International audience; This paper proposes a computational model for learning robot control and sequence planning based on the ideo-motor principle. This model encodes covariation laws between sensors and motors in a modular fashion and exploits the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9668ed60fab0cc97537d024f01067ebe
https://doi.org/10.1109/tcds.2016.2647439

Learning and Composing Primitive Skills for Dual-Arm Manipulation

Autor: Yvan Petillot, Michael Mistry, Paola Ardón, Èric Pairet

Publikováno v: Towards Autonomous Robotic Systems ISBN: 9783030238063
TAROS (1)

In an attempt to confer robots with complex manipulation capabilities, dual-arm anthropomorphic systems have become an important research topic in the robotics community. Most approaches in the literature rely upon a great understanding of the dynami

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::51582c0a142228ed4bce18c9648d3ba1
https://doi.org/10.1007/978-3-030-23807-0_6

Programming everyday task using primitive skills and generative model of movement demonstrated by human

Autor: Pham Ngoc Hung, Takashi Yoshimi

Publikováno v: 2017 International Conference on Advanced Robotics and Intelligent Systems (ARIS).

This manuscript describes a method to program everyday manipulation tasks for the robots from human demonstration by using a movement generating model and primitive skills. The hand movement in the demonstrated task is recorded by using a hand motion

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::25911251ac90cf281126d4aeb08920f8
https://doi.org/10.1109/aris.2017.8297177