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cs.RO2026
AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models
Mozhgan Pourkeshavarz, Tianran Liu, Nicholas Rhinehart
Simulation with realistic traffic agents is essential for validating autonomous driving systems. Existing data-driven simulators learn agent behavior from higher-level abstractions…
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.…