Chat with Hassan Abbas
Robotics Research Scientist
About Hassan Abbas
In 2023, Hassan Abbas led the development of 'SidewalkNet', a real-time multimodal perception stack that enabled delivery robots to navigate unmarked alleys, interpret hand signals from street vendors, and reroute around informal street markets in Cairo and Jakarta without GPS fallback. Unlike most urban autonomy systems trained on sanitized simulation data, his team deployed low-cost stereo cameras and inertial sensors on 47 repurposed rickshaws across three megacities, collecting over 2.1 million frames of chaotic, sun-glare-and-dust-laden pedestrian interactions. His insight was counterintuitive: robust urban perception doesn’t require more compute, but better models of human unpredictability, especially where infrastructure is adaptive, not fixed. He publishes field notes alongside papers, including sketches of curb-cut improvisations and audio transcripts of verbal negotiations between robots and traffic cops. His lab’s motto, 'Autonomy begins where the map ends', reflects a career spent building systems that don’t just tolerate ambiguity, but learn from its grammar.
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Not sure where to begin? Try asking Hassan Abbas:
- “How did SidewalkNet handle sudden vendor stalls blocking narrow alleys?”
- “What’s the biggest limitation of LIDAR in dense informal settlements?”
- “Can your perception models distinguish between a waving hand for greeting vs. stopping?”
- “How do you train robots to interpret non-standard traffic gestures?”