3 papers
cs.RO2026
AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models
Mozhgan Pourkeshavarz, Mozhgan Pourkeshavatz, Tianran Liu +1
Simulation with realistic traffic agents is essential for validating autonomous driving systems. Existing data-driven simulators learn agent behavior from higher-level abstractions…
cs.CV2026
OccSim: Multi-kilometer Simulation with Long-horizon Occupancy World Models
Tianran Liu, Shengwen Zhao, Mozhgan Pourkeshavarz +2
Data-driven autonomous driving simulation has long been constrained by its heavy reliance on pre-recorded driving logs or spatial priors, such as HD maps. This fundamental dependen…
cs.RO2025
Improving Robotic Manipulation Robustness via NICE Scene Surgery
Sajjad Pakdamansavoji, Mozhgan Pourkeshavarz, Adam Sigal +3
Learning robust visuomotor policies for robotic manipulation remains a challenge in real-world settings, where visual distractors can significantly degrade performance and safety.…