James R. Han — accepted AI-conference papers

2026

SICNav: Safe and Interactive Crowd Navigation Using Model Predictive Control and Bilevel Optimization (Abstract Reprint)

Sepehr Samavi, James R. Han, Florian Shkurti, Angela P. Schoellig

AAAI 2026technical

Robots need to predict and react to human motions to navigate through a crowd without collisions. Many existing methods decouple prediction from planning, which does not account for the interaction between robot and human motions and can lead to the robot getting stuck. We propose SICNav, a Model Pr

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2025

DR-MPC: Deep Residual Model Predictive Control for Real-World Social Navigation

James R. Han, Hugues Thomas, Jian Zhang, Nicholas Rhinehart, Timothy D. Barfoot

RA-L 2025

How can a robot safely navigate around people with complex motion patterns? Deep Reinforcement Learning (DRL) in simulation holds some promise, but much prior work relies on simulators that fail to capture the nuances of real human motion. Thus, we propose Deep Residual Model Predictive Control (DR-

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