Work

• Developed inverse dynamic game formulations for multi-agent systems, modeling agent interactions through generalized Nash equilibria
• Designed a Bayesian inference framework that represents latent agent intent probabilistically rather than deterministically
• Built a neural inference architecture combining a Variational Autoencoder (VAE) with DiNOv3-based visual embeddings to process high-dimensional image observations
• Integrated perception and dynamics by conditioning the inverse game on both trajectories and learned visual scene representations
• Implemented and evaluated the framework in CARLA, enabling realistic multi-agent autonomous driving experiments
• Performed Monte Carlo evaluations to quantify uncertainty, robustness, and inference accuracy across ambiguous interaction scenarios

Impact

This work demonstrates that incorporating uncertainty and multi-modal perception into inverse games significantly improves intent inference in interactive environments. The resulting predictions are more robust to ambiguity, making the approach well-suited for safety-critical autonomous systems where incorrect assumptions about agent intent can lead to failure.

Program

The Moncrief Summer Research Internship at the Oden Institute is a competitive undergraduate research program that immerses students in full-time computational science research under faculty mentorship, culminating in a formal poster presentation.

Project Summary

This project explored how agent intent can be inferred from multi-modal observations—specifically top-down images and partial trajectory data—using simplified driving scenarios. The focus was on demonstrating, in a controlled toy environment, that combining visual context with motion information enables better reasoning about agent goals than trajectories alone.

Work

• Proposed an inference framework that estimates agent intent from top-down image observations and trajectory data
• Formulated and solved an amortized maximum likelihood problem by differentiating through a Nash game solver
• Implemented sequence modeling with LSTMs to encode trajectory information
• Designed custom VAEs to represent latent intent variables
• Built CNN-based perception models to extract structured information from image observations
• Validated the approach in toy multi-agent driving environments to demonstrate improved intent recovery under ambiguity

Impact

This work provides an early demonstration that multi-modal perception materially improves inverse game inference, even in minimal settings. The results motivate extending inverse game frameworks beyond trajectory-only data and toward richer perceptual inputs, laying groundwork for safer and more informed multi-agent decision-making in autonomous systems.