Dexterity says that after NVIDIA acceleration, the visual perception pipeline of its world model Foresight cut per-cycle latency from 1508ms to 90ms, a 17x speedup.
Why it matters: The original breaks down latency data and optimization methods for each stage of the pipeline, giving robotics developers a GPU acceleration approach they can adapt to their own perception systems.
FieldAI presents its collaboration with the NVIDIA Omniverse ecosystem, using NuRec to reconstruct multimodal data collected during robot operations—including vision, depth, and LiDAR—into high-fidelity, interactive 3D scenes without a separate scanning step.
Odyssey ML announced it has received investment from NVIDIA's venture arm NVentures and Samsung Next to accelerate its research on general world simulators. Samsung Next reposted the news, saying it is excited to be part of Odyssey ML's journey, and included a link to the original tweet: https://x.com/odysseyml/status/2021997012938580367.
Diligent Robotics announced the launch of Moxi 2.0, which the company says delivers 10 times the compute of Moxi 1.0 and is built on the NVIDIA Thor platform. The platform was trained on three years of real-world hospital deployment data and more than 1.25 million deliveries; Moxi currently delivers medications and lab samples for nurses and pharmacy staff at more than 25 hospitals across the U.S.
This article explains NVIDIA's open-source humanoid robot foundation model Isaac GR00T N1, unveiled at the March 2025 GTC conference, along with the accompanying GR00T Blueprint synthetic data toolchain and Newton physics engine.
Field AI Research Institute (FAIRI) is collaborating with NVIDIA to study robotic embodied question answering (EQ&A) in dynamic environments. The team uses the NVIDIA Omniverse platform, Isaac Sim, Omniverse Replicator, and the USD Search API to build physically accurate 3D office scenes, simulate changes in the environment over time, and plans to open-source this long-term embodied question answering dataset.
FieldAI claims to have developed the first risk-bounded Field Foundation Models (FFM) robot foundation model, which enables multi-form robots to operate autonomously in hazardous, off-road, and other unstructured industrial environments without prior maps, GPS, or cloud communication, and has already been deployed at hundreds of sites.