Tianyu Li

Real-world driving scenarios can now be seamlessly translated into controllable closed-track tests, enabling more realistic validation of automated driving systems.

Users can now interactively critique and optimize their AI-driven data queries, transforming opaque analytics into a transparent, controllable process.

RDVSv2 reveals that existing RGB-D VSOD methods struggle significantly with its challenging scenarios, setting a new standard in the field.

Training neural surrogates without labeled data can achieve remarkable accuracy in predicting complex thermo-fluid fields, slashing model development costs.

Current robotic grasping methods struggle, with success rates under 70% in complex scenarios that demand reasoning and semantic understanding.

Achieving state-of-the-art accuracy in fluid dynamics predictions, ME-GNN dramatically reduces computational costs associated with complex geometries.

Post-training on synthesized safety-critical scenarios can dramatically enhance the reliability of autonomous driving systems, reducing failures in rare but critical events.

RoboNaldo slashes free-kick shot errors by nearly 50% while achieving ball speeds that rival professional soccer players.

Overcome the static train-then-freeze paradigm with a new test-time adaptation framework that significantly improves camouflaged object detection in unseen environments.

Achieve spatially grounded natural language descriptions of urban development with PTNet, a new model that understands change semantics better than existing methods.

Achieve 2.5x higher success in long-horizon robotic manipulation with 90% less data and compute by explicitly aligning training and deployment distributions.