✦ Strategic Overview
Commercial Goal
Full commercialization of spatial AI by 2026, targeting Robotics & AVs.
Core Challenges
Solving data costs, compute overhead, and the critical Sim-to-Real gap.
Methodology
Multimodal sensor fusion combined with NeRF & Gaussian Splatting.
1. Multimodal Data Ingestion
Captures physical context via RGBD, LiDAR, and IMUs to overcome real-world uncertainties. Focuses on environmental adaptability and safety through real-time fusion.
Key Research
2. Sensor Fusion
Reducing latency and preventing cascading errors via Bayesian filters and automatic calibration.
3. Reality Capture
State-of-the-art scene reconstruction using NeRF and Gaussian Splatting (3DGS).
4. World-Model Architectures
Modularization of JEPA, memory, and action modules. Enhancing latent space prediction for physical mass and motion.
5. Generative Simulation
Using simulators like DreamerV3 and GAIA to expand rare case coverage. Aiming for 5x robotics training speed.
6. Training & Algorithmic Mix
Combining Self-Supervised Learning (SSL) with RL. Targeting a 50% improvement in data efficiency.
7. Sim-to-Real
Iterative calibration and domain randomization. Goal: 10% RMSE reduction in AVs.
- ■ Int. J. Appl. Earth Obs (2025): 25% style transfer accuracy.
- ■ Scientific Reports (2025): Medical micro-drilling recognition.
8. Compute & MLOps
Optimization of GPU clusters and model distillation for 50% edge latency reduction.
9. Governance
Reliability via formal verification. 40% risk reduction in mission-critical deployments.
Future Outlook
R&D for 2025-2026 is fundamentally shifting World Models into physical space. The integration of synthetic data and self-supervised learning act as the primary engines, while rigorous safety governance ensures these systems can be trusted in our most critical infrastructures.