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Showing 1–7 of 7 results
Advanced filters: Author: Sami Haddadin Clear advanced filters

  • Despite decades of research, autonomous robotic manipulation skills remain limited, especially for complex tasks such as insertion or bending of materials. Johannsmeier et al. introduce a taxonomy of manipulation skills that synthesizes tactile behaviours from process specifications, achieving high robustness and performance with minimal learning time.

    • Lars Johannsmeier
    • Samuel Schneider
    • Sami Haddadin
    ResearchOpen Access
    Nature Machine Intelligence
    Volume: 7, P: 916-927

  • Current limb-driven methods often result in suboptimal prosthetic motions. Kühn and colleagues develop a framework called synergy complement control (SCC) that advances prosthetics by learning ‘cyborg’ limb-driven control, ensuring natural coordination. Validated in diverse trials, SCC offers reliable and intuitive enhancement for limb functionality.

    • Johannes Kühn
    • Tingli Hu
    • Sami Haddadin
    ResearchOpen Access
    Nature Machine Intelligence
    Volume: 6, P: 481-492

  • It is challenging to compare how well robots perform a task, as the evaluation depends on the process and skills required. It is proposed to group robots into a taxonomy based on their performance on a set of embodied skill benchmarks.

    • Robin Jeanne Kirschner
    • Kübra Karacan
    • Sami Haddadin
    ResearchOpen Access
    Nature Machine Intelligence
    Volume: 7, P: 459-470

  • There is a need to consider how AI developers can be practically assisted in identifying and addressing ethical issues. In this Comment, a group of AI engineers, ethicists and social scientists suggest embedding ethicists into the development team as one way of improving the consideration of ethical issues during AI development.

    • Stuart McLennan
    • Amelia Fiske
    • Alena Buyx
    Comments & Opinion
    Nature Machine Intelligence
    Volume: 2, P: 488-490