Concepedia

Contact-safe Learning

Contact-Safe Learning is a robot learning framework that ensures physical interactions with humans or objects occur within quantified safety bounds, distinguishing itself from general safe learning approaches by explicitly governing mechanical contact dynamics through real-time compliance control and impact anticipation.

4

Publications

78

Citations

17

Authors

5

Institutions

Publications per year

2017–2026

2

Parents

Publications

4

Learning peripersonal space representation through artificial skin for avoidance and reaching with whole body surface

Alessandro Roncone, Matej Hoffmann, Ugo Pattacini et al. · 2015 · 23 citations

, , +17

Mimetic communication with impedance control for physical human-robot interaction

Dongheui Lee, Christian Ott, Yoshihiko Nakamura · 2009 · 19 citations

, , +17

Uncertainty-Aware Contact-Safe Model-Based Reinforcement Learning

Cheng-Yu Kuo, Andreas Schaarschmidt, Yunduan Cui et al. · IEEE Robotics and Automation Letters · 2021 · 18 citations

, , +17

Collaboration of multiple SCARA robots with guaranteed safety using recurrent neural networks

Yuhong He, Xiaoxiao Li, Zhihao Xu et al. · Neurocomputing · 2021 · 18 citations · Full text

, , +16

RMPs for Safe Impedance Control in Contact-Rich Manipulation

Seiji Shaw, Ben Abbatematteo, George Konidaris · 2022 International Conference on Robotics and Automation (ICRA) · 2022 · 10 citations

, , +16

1–5 of 4

Authors

17

  1. YN
    Yoshihiko Nakamura

    The University of Tokyo, Japan

    1 publications · 19 citations

    1

    h-index

  2. DL
    Dongheui Lee

    Technical University of Munich, Germany

    1 publications · 19 citations

    1

    h-index

  3. CK
    Cheng-Yu Kuo

    1 publications · 18 citations

    1

    h-index

  4. ZX
    Zhihao Xu

    Guangdong Institute of Intelligent Manufacturing, China

    1 publications · 18 citations

    1

    h-index

  5. AS
    Andreas Schaarschmidt

    1 publications · 18 citations

    1

    h-index

1–5 of 17

Institutions

5

  1. The University of Tokyo

    Tokyo, Japan

    1 h-index

    3 publications

    57 citations

  2. Swansea University

    Swansea, United Kingdom

    1 h-index

    2 publications

    36 citations

  3. Guangdong Academy of Sciences

    Guangzhou, China

    1 h-index

    3 publications

    54 citations

  4. Guangdong Institute of Intelligent Manufacturing

    Guangzhou, China

    1 h-index

    3 publications

    54 citations

  5. Italian Institute of Technology

    Genoa, Italy

    1 h-index

    4 publications

    92 citations

  6. Brown University

    Providence, United States

    1 publications

    10 citations

1–6 of 5

Venues

  1. Neurocomputing

    1 publications · 18 citations

    1

    h-index

  2. IEEE Robotics and Automation Letters

    1 publications · 18 citations

    1

    h-index

  3. 2022 International Conference on Robotics and Automation (ICRA)

    Conference · 1 publications · 10 citations

1–3