Real-Time 4D Gaussian World Foundation Models (WFM) for Autonomous Physical AI & Embodied Robotics.
Current generative AI excels in 2D pixels and text tokens, but collapses when applied to real-world robots operating in three-dimensional space and dynamic time (4D).
2D video diffusion models hallucinate physics, creating inconsistent geometry, phantom collisions, and inaccurate friction estimation.
Diffusion-based world generators take 1.2 to 4.5 seconds per frame. Real-world robotics requires sub-10ms closed-loop feedback.
Robots cannot carry multi-kilowatt servers. Physical AI must compile onto embedded low-power edge accelerators (15W to 60W).
Instead of rendering flat 2D pixels, VoxelDyn models physical reality directly as spatio-temporal 4D Gaussian primitives with explicit velocity, mass, and collision boundaries.
Maintains high-density 3D metric depth and volumetric occupancies, giving robot motion planners guaranteed collision-free trajectory corridors.
Custom FP8 CUDA rasterization executes at 144 FPS with sub-4ms actuation latency, unlocking instantaneous reflex loops for humanoid bipedal balancing.
VoxelDyn is natively engineered to maximize GPU compute efficiency across cloud training and edge deployment.
Warp-level shuffles and cooperative groups delivering 3.4x faster Gaussian splat sorting.
FP8 Transformer execution engine reducing VRAM footprint by 48% on Hopper and Ada Lovelace.
Direct OpenUSD connectors for million-scenario synthetic training and sim-to-real transfer.
Concurrent multi-model orchestration for multi-camera robot perception fleets.
275 TOPS of edge physical AI within a lightweight, battery-efficient 15W to 50W envelope.
Multi-node H100 SXM5 training cluster powered by NVLink 4.0 900 GB/s cross-GPU interconnect.
VoxelDyn delivers unprecedented throughput and predictive fidelity compared to legacy spatial and diffusion models.
| Architecture | Actuation Latency | Throughput (FPS) | Physics Consistency | Edge Deployment |
|---|---|---|---|---|
| Video Diffusion WFM (e.g. Sora/Runway) | 1,800 ms | 0.55 FPS | Poor (Hallucinatory) | No (Requires 8x H100) |
| Occupancy Grid Voxel Baselines | 32 ms | 30 FPS | Moderate (Coarse Grid) | Partial (High Memory) |
| VoxelDyn Core™ (Ours) | 3.8 ms | 144 FPS | 99.4% Metric Precision | Yes (Jetson AGX Orin 15W) |
The convergence of humanoid robotics, industrial automation, and spatial computing is creating an insatiable demand for GPU-native Physical AI foundations.
Warehouse logistics, manufacturing assembly, and general-purpose service robots.
GPS-denied visual-inertial navigation and dynamic obstacle avoidance.
Factory simulation in Omniverse for offline reinforcement learning.
Over 1,200 hours of synthetic physics simulation training logged, with closed alpha partner pilots underway.
Scalable recurring software licensing for robotics OEMs, paired with high-margin cloud simulation APIs.
Annual per-robot recurring license ($1,200 to $4,800 / year / robot) for embedded Jetson execution on deployed autonomous fleets.
Pay-per-hour synthetic 4D scenario generation hosted on DGX Cloud. Allows robotics developers to train manipulation policies at 100x wall-clock speed.
Technical leadership driven by single-minded execution, deep-tech research velocity, and hands-on CUDA architecture.
Ex-Stanford AI Lab researcher specializing in neural rendering and autonomous perception. 8+ years hands-on experience developing low-level CUDA kernels, Triton server pipelines, and TensorRT engines. Author of 6+ CVPR and NeurIPS publications with over 1,800 academic citations.
How high-performance GPU compute clusters and hardware dev kits accelerate VoxelDyn's path to commercial scale.
Allocated to pre-training our 14B and 32B 4D Gaussian Foundation Model on 16x H100 SXM5 nodes.
Benchmarking and optimizing EdgeRT sub-15W runtime on next-gen Orin Nano and AGX Orin 64GB silicon.
Deploying simulation models across Tier-1 robotics OEMs and industrial logistics fleets.