5Meta AI Blog·
Meta's SAM and DINOv3 deployed in ARPA-H-funded assistive robotics platform at University of Pittsburgh
The University of Pittsburgh's Human Engineering Research Laboratories (HERL), with up to $41.5M in ARPA-H funding, is building RAMMP, a robotic assistive mobility and manipulation platform for wheelchair users that integrates Meta's open-source vision models SAM and DINOv3. The project deploys these models on edge hardware for real-time object detection, navigation assistance, and natural language interaction, addressing engineering constraints like battery life, heat, and limited compute. This is a concrete production deployment case demonstrating open-weights vision models running on resource-constrained robotics hardware in a safety-critical assistive context.
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UPenn PRONTO Team Uses Meta's SAM 2 and DINO for Autonomous Military Medical Triage in DARPA Challenge
The University of Pennsylvania's PRONTO team is applying Meta's Segment Anything Model 2 (SAM 2) and DINO/Grounding DINO models to autonomous robotic triage in DARPA's three-year mass casualty incident challenge. The multi-robot system uses drones and ground robots to locate victims, then runs parallel injury classification pipelines combining SAM, DINO, and pose estimation to assess heart rate, respiration, wounds, and amputations without requiring labeled training data. Results are surfaced to first responders via a mobile interface for real-time prioritization. Phase 2 concluded in October 2025, with Phase 3 expected to push toward deployment-ready performance.
Meta's SAM 3 and DINOv3 power real-time scientific image segmentation at DOE national labs
Lawrence Berkeley National Laboratory's SYNAPS-I project, part of the White House Genesis Mission initiative, has deployed Meta's Segment Anything Model 3 (SAM 3) and DINOv3 on 300 A100 GPUs at national supercomputing facilities to automate segmentation of X-ray and neutron science imagery. The pipeline reduces expert annotation time from weeks or months to approximately 15 minutes per dataset, enabling real-time analysis during live experiments. A demonstration on grapevine drought resilience using micro-CT scans showed that month-long per-timestep annotation workflows now complete in 15 minutes. Meta's open-source release of both models was critical, as national labs require on-premises deployment on secure government infrastructure.
Meta SAM 3 (Segment Anything Model 3) Released on GitHub
Meta / Facebook Research has released SAM 3, the third generation of their Segment Anything Model, with code for inference and finetuning, pretrained model checkpoints, and example notebooks. The repository has accumulated over 10,000 stars with strong daily momentum (+93). SAM 3 continues Meta's open-weights tradition in computer vision foundation models. No accompanying paper or technical blog post is referenced in this item.
SAM 3.1: Meta Releases Faster Real-Time Video Segmentation Model with Object Multiplexing
Meta has released SAM 3.1, an incremental update to Segment Anything Model 3, introducing object multiplexing that allows tracking up to 16 objects in a single forward pass. This doubles video processing throughput from 16 to 32 FPS on a single H100 GPU, reducing GPU resource requirements and enabling real-time tracking on smaller hardware. SAM 3.1 is a drop-in replacement for SAM 3 and is available via updated model checkpoints and codebase. The broader SAM 3 release also includes text and exemplar prompting, a new Segment Anything Playground, the SA-Co evaluation dataset, and SAM 3D for 3D reconstruction.
Reachy Mini - The Open-Source Robot for Today's and Tomorrow's AI Builders
Hugging Face has published a blog post introducing Reachy Mini, an open-source desktop robot designed for AI developers and researchers. The post positions the robot as a platform for building and testing embodied AI applications. As an open-source hardware/software project, it targets the growing intersection of robotics and AI model deployment.
Meta and World Resources Institute Release Canopy Height Maps v2 Using DINOv3 Self-Supervised Vision Model
Meta AI and the World Resources Institute have released Canopy Height Maps v2 (CHMv2), an open-source global forest mapping system powered by DINOv3, Meta's self-supervised vision model pre-trained on SAT-493M, a large satellite imagery dataset. The new model improves R² accuracy from 0.53 to 0.86 over the previous DINOv2-based version, with better performance on tall trees and greater geographic consistency. CHMv2 is already being adopted by the UK Forestry Commission, the European Commission's Joint Research Centre, and multiple US city planning initiatives. The model, maps, and dataset are publicly available.
Forest Research Deploys DINOv2 for National-Scale Tree Canopy Monitoring in England
Forest Research, the UK Forestry Commission's research agency, is using Meta's DINOv2 computer vision model—trained on 18 million satellite images in collaboration with the World Resources Institute—to build enhanced tree canopy height maps at 1-meter resolution for England. The approach aims to replace expensive LiDAR and survey data with open-source AI-derived canopy height models applied to national aerial photography, enabling rolling three-year monitoring cycles. The deployment supports the UK government's Environmental Improvement Plan targets and the Natural Capital and Ecosystem Assessment program. Meta also announced DINOv3 as a successor to further improve visual intelligence for such applications.
DEED framework bridges lab-to-store gap for humanoid retail robots using data-efficient VLA post-training
Researchers present DEED (Data-Efficient Post-Training and Experience-Driven Learning), a systems-level framework for deploying Vision-Language-Action humanoid robots in real retail environments. The approach is evaluated on a supermarket chip-restocking task using a Unitree G1-Edu humanoid and NVIDIA's GR00T N1.6 foundation model, combining data-efficient post-training, experience-driven refinement adapted from RECAP, and a latent-space analysis tool. Key finding: bridging lab-to-real performance gaps is primarily a systems integration challenge rather than an architectural one, achievable with careful data design on a single GPU.