Accelerative Synthetic Data Generation
Diffusion-based filtering to detect inauthentic synthetic videos 9× faster with early-exit pipelines. 75% compute savings, 6× faster generation.
A catalogue of projects across foundation models, multi-agent autonomy, and field robotics. Sorted by domain, not by chronology.
Diffusion models, synthetic data, and foundation models.
Diffusion-based filtering to detect inauthentic synthetic videos 9× faster with early-exit pipelines. 75% compute savings, 6× faster generation.
Intermediate optical flow estimation for orthomosaic creation from sparse aerial images. High-quality crop health analysis with reduced overlap (50% vs. 70-80%).
Combining foundation models with embodied control.
Augmented real-world video datasets for VLA training, addressing data scarcity with minimal trajectories. Enables sample-efficient policy training.
Photoreal NVIDIA Omniverse simulation with generative animal behaviors, herd dynamics, and drone responses. Reduces field deployment costs.
Synthetic multimodal dataset for wildlife behaviors using world models. Boosts robust classification from scarce data.
Autonomous systems, multi-agent coordination, robot control.
Segmentation neural network and depth sensing for lane centering on uneven terrain. Robust navigation with 40° slip compensation in real fields.
CNN + RL for heterogeneous agents in agriculture. 60% scouting reduction, 80% accuracy, 4.8× labor savings, 36% farmer profit boost.
Level 3.5 motion planning using road-marking intent detection and graph-theoretic coordination. Communication-free across 255 scenarios.
Distributed online patrol with scalable trajectories balancing priority and non-priority site coverage. Sim-to-real validated.
Communication-free community formations for multi-robot swarms. Validated in simulation, experiments, and lab tests.
Multi-agent RL coordination for UAVs in detailed crop health assessment. Scalable deployment without synthetic data dependency.