Dynamic Route Guidance in Vehicle Networks by Simulating Future Traffic Patterns
Abstract: Roadway congestion leads to wasted time and money and environmental damage. One possible solution is adding more roadway capacity, but this can be impractical especially in urban environments and still may not make up for a poorly-calibrated traffic signal schedule. As such, it is becoming increasingly important to use existing road networks more efficiently. [...]
Correspondence-Preserving Transformers for Scalable 3D Lifting
Abstract: Takeo Kanade's famous quip - to infer geometry or motion from images, you must first know what in one image corresponds to what in another, has guided geometric vision for three decades. Deep learning seemed to bypass this: methods in 2017-2019 lifted 2D to 3D using only reprojection loss, exploiting an implicit bias toward smooth [...]
Empirically Grounded LLM-based Virtual Patients for Psychotherapy Training: Design, Modeling, and Evaluation
Abstract: The need for mental health care continues to outpace the supply of trained psychotherapists, while psychotherapy training remains constrained by limited supervision time and scarce opportunities for repeated, feedback-rich practice in realistic scenarios. Simulation-based training can mitigate these constraints, but actor-based standardized patients are costly and difficult to scale, and many clinically challenging moments [...]
Plan What You Can, Learn What You Must: Interleaving Planning and Learning for Multi-Robot Manipulation
Abstract: Multi-robot manipulation is becoming an inevitability of modern robotics. As hardware costs fall, the barrier to deploying robot teams has shifted from economics to algorithmic capability. To fulfill their promise, multi-robot systems must jointly reason about geometric coordination, contact interactions, task assignments, and scene dynamics, while adapting to variable team sizes and diverse robot [...]