Robotik

MN·

NachrichtenMIT News · Robotics·29.09.2026

Powered by muscle cells, a paper-thin robot swims through watery maze

MIT engineers’ new aquabot offers a way to design small, efficient “biohybrid” robots.

NMI

FachartikelNature Machine Intelligence·01.10.2026

Shifting from knowledge retrieval to evidence exploration and synthesis

Nature Machine Intelligence, Published online: 01 October 2026; doi:10.1038/s42256-026-01313-w Biomedical discovery has entered an era in which the limiting resource is no longer data, but our ability to integrate and interpret evidence. DeepEvidence, a new deep research agent, goes beyond retrieving facts and constructs explicit representations of scientific evidence.

NMI

FachartikelNature Machine Intelligence·01.10.2026

Large language models discover complementary heuristics for combinatorial optimization

Nature Machine Intelligence, Published online: 01 October 2026; doi:10.1038/s42256-026-01307-8 Huatian Gong and colleagues developed LACE, a large language model-based framework that designs optimization algorithms. It builds a verified problem contract, then evolves a portfolio of complementary heuristics that together solve problems that no single method can handle.

NMI

FachartikelNature Machine Intelligence·30.09.2026

Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pretraining

Nature Machine Intelligence, Published online: 30 September 2026; doi:10.1038/s42256-026-01309-6 Alonso-Monsalve et al. demonstrate that self-supervised pretraining helps deep learning models to interpret complex neutrino detector events, improving classification, reconstruction and data efficiency while enabling transfer across different neutrino detector technologies.

NMI

FachartikelNature Machine Intelligence·28.09.2026

Minute-scale training for microrobot navigation

Nature Machine Intelligence, Published online: 28 September 2026; doi:10.1038/s42256-026-01305-w A vectorized simulator and structured reward framework enable microrobot navigation policies to be trained within minutes and transferred without retraining across robots and environments.

NMI

FachartikelNature Machine Intelligence·28.09.2026

Regional climate risk assessment from climate models using probabilistic machine learning

Nature Machine Intelligence, Published online: 28 September 2026; doi:10.1038/s42256-026-01308-7 A generative AI framework called GenFocal is introduced for climate downscaling, producing realistic fine-scale weather from coarse projections and improving regional risk estimates of compound extremes such as heatwaves and tropical cyclones.

NMI

FachartikelNature Machine Intelligence·28.09.2026

Task-structured modularity emerges in artificial networks and aligns with brain architecture

Nature Machine Intelligence, Published online: 28 September 2026; doi:10.1038/s42256-026-01306-9 Wu et al. show that neural networks learning multiple tasks organize into specialized modules, particularly under capacity constraints. Incremental multitask learning strengthens this modularity and produces architectures resembling brain networks.