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Robot safety Latest coverage

Robotics coverage relating to robot safety.

64 selected reportsLast 30 days 56 recordsAll coverage 263 records

updated

Signal archive — Page 4

2026-09-29Tue61–64 records
  1. Tech Xplore — Robotics82

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

    MIT engineers have developed a soft, paper-thin robot that swims through water using a single layer of genetically engineered muscle cells. The robot's 'fins' are covered with live muscle cells that twitch in response to light, allowing it to flap and navigate through a watery maze. The study demonstrates the potential of biohybrid robots for delicate aquatic tasks, with the robot achieving a swimming speed of about four times its body length per minute.

2026-08-28Fri
2026-07-22Wed
  1. NASA — Robotics68

    Robotic Servicing Mission Launches with NASA Support

    The Robotic Servicing of Geosynchronous Satellites (RSGS) mission, supported by NASA, has launched with a SpaceX Falcon 9 rocket. The Mission Robotic Vehicle (MRV) will service satellites in geosynchronous orbit using advanced robotics, including twin dexterous arms developed by the U.S. Naval Research Laboratory. NASA's support includes simulation tools, software analysis, and flight operators to enable in-orbit servicing and extend satellite lifespans.

2026-03-09Mon
  1. NVIDIA — Robotics model repository updates75

    NVIDIA releases SOMA-X v0.3.0 unified parametric human body model framework

    NVIDIA has released SOMA-X v0.3.0, a unified framework for parametric human body models that decouples identity representation from pose parameterization. The release supports six identity backends including SOMA-shape, SMPL, and SMPL-X, and introduces native hand layers with interoperability for user-supplied MANO models. Designed for commercial use under the Apache 2.0 license, the model enables real-time simulation pipelines on NVIDIA GPUs with throughput exceeding 7,000 meshes per second on an A100. The core pipeline relies on closed-form analytical modules rather than learned neural networks, though an optional shallow MLP can be enabled for surface correction.

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