Haoang Li

Drones can navigate uncertain winds and complete deliveries more reliably by dynamically re-routing based on real-time energy expenditure and risk assessment.

VLN agents can navigate more effectively by predicting their future states and proactively planning based on forecasted semantic map cues, rather than relying solely on historical context.

Robots can now nimbly navigate complex, multi-floor environments without prior training, thanks to a new strategy that dynamically switches between exploration, recovery, and memory recall.

Ditch slow, multi-step video generation: S-VAM distills the structured generative priors of multi-step denoising into a single forward pass for real-time robot action prediction.

Achieve state-of-the-art outlier robustness and threshold resilience in geometric estimation with a surprisingly efficient branch-and-bound approach.

Achieve higher accuracy in dual-arm robot calibration by unifying coordinate and kinematic parameter estimation within a single Lie-algebraic framework, eliminating artificial error separation.

By aligning latent representations with multiple visual foundation models, FRAPPE offers a more scalable and data-efficient way to imbue generalist robotic policies with robust world-awareness.