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DIMAG AI LABS

// Model Zoo

Pre-trained
Models.

Explore a library of pre-trained models for robotic tasks — optimized for compatibility with RViz, Gazebo, ROS2 simulators, and NVIDIA Isaac Sim.

⌘K
  • Navigation

    OL-Nav-Suite v3

    Large-scale RGB-D navigation backbone enriched with physics-aware world models and semantic understanding for robust indoor + outdoor navigation.

    Params · 1.3BBench · 94.2 SR · HM3DLicense · Commercial
    OL-Nav-Suite v3
  • Grasping

    OL-Grasp Pro

    Comprehensive grasping policy trained on 4.2M demonstrations across 12 embodiments. Includes material properties and failure prediction heads.

    Params · 780MBench · 0.91 success · RLBenchLicense · Commercial
    OL-Grasp Pro
  • Locomotion

    OL-Loco Patterns

    Multi-terrain locomotion priors with enhanced physics modelling, ground interaction forces, and adaptive gait patterns.

    Params · 420MBench · +18% vs SOTALicense · Commercial
    OL-Loco Patterns
  • Visual Understanding

    OL-Vision World

    Egocentric vision foundation model trained on 48K hours of synced multimodal capture. Drop-in backbone for VLA and world-model heads.

    Params · 2.4BBench · EK-100 SOTALicense · Commercial
    OL-Vision World
  • Mapping

    OL-Map Continuous

    Online dense mapping with implicit neural fields, optimized for HMD-class compute and streaming reconstruction.

    Params · 340MBench · 2.1cm RMSELicense · Commercial
    OL-Map Continuous
  • Motion Planning

    OL-Plan Latent

    Latent diffusion planner conditioned on hand pose, gaze, and action labels. Ships with RViz / Isaac Sim / Gazebo integrations.

    Params · 1.1BBench · 0.88 task SRLicense · Commercial
    OL-Plan Latent

Need a custom
checkpoint?

Every model in the zoo can be fine-tuned on your embodiment with our custom-collection pipeline. Talk to research.

Pre-trained models for embodied AI and robotics

Model zoo covering navigation, grasping, locomotion, mapping, visual understanding, and motion planning for ROS2 and Isaac Sim.