5 papers
Directional Pullback Regularization for Long-Horizon Rollout of Latent World Models in Autonomous Driving
Shatadal Mishra, Ahmadreza Moradipari, Nejib Ammar
Learned world models for autonomous driving operate in unconstrained latent spaces, yet the physical systems they represent are governed by strict non-holonomic constraints: ground…
Network-Efficient World Model Token Streaming
Shatadal Mishra, Ahmadreza Moradipari, Nejib Ammar
Generative driving world models rely on compact latent state representations that must be efficiently transmitted and synchronized across distributed compute and connected vehicles…
IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-Tuning
Dechen Gao, Hang Wang, Hanchu Zhou +5
Imitation learning (IL) and reinforcement learning (RL) each offer distinct advantages for robotics policy learning: IL provides stable learning from demonstrations, and RL promote…
Tolerance of Reinforcement Learning Controllers against Deviations in Cyber Physical Systems
Changjian Zhang, Parv Kapoor, Eunsuk Kang +5
Cyber-physical systems (CPS) with reinforcement learning (RL)-based controllers are increasingly being deployed in complex physical environments such as autonomous vehicles, the In…
Investigating Robustness in Cyber-Physical Systems: Specification-Centric Analysis in the face of System Deviations
Changjian Zhang, Parv Kapoor, Romulo Meira-Goes +5
The adoption of cyber-physical systems (CPS) is on the rise in complex physical environments, encompassing domains such as autonomous vehicles, the Internet of Things (IoT), and sm…