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SCENIC: An Open-Source Probabilistic Programming System for Data Generation and Safety in AI-Based Autonomy

Edward Kim · Sanjit Seshia · Daniel Fremont · Jinkyu Kim · Kimin Lee · Hazem Torfah · Necmiye Ozay · Parasara Sridhar Duggirala · Marcell Vazquez-Chanlatte

Arch 307-308
[ ] [ Project Page ]
Mon 17 Jun 9 a.m. PDT — noon PDT


Autonomous systems, such as self-driving cars or intelligent robots, are increasingly operating in complex, stochastic environments where they dynamically interact with multiple entities (human and robot). There is a need to formally model and generate such environments in simulation, for use cases that span synthetic training data generation and rigorous evaluation of safety. In this tutorial, we provide an in-depth tutorial on Scenic, a simulator-agnostic probabilistic programming language to model complex multi-agent, physical environments with stochasticity and spatio-temporal constraints. Scenic has been used in a variety of domains such as self-driving, aviation, indoor robotics, multi-agent systems, and augmented/virtual reality. Using Scenic and associated open source tools, one can (1) model and sample from distributions with spatial and temporal constraints, (2) generate synthetic data in a controlled, programmatic fashion to train and test machine learning components, (3) reason about the safety of AI-enabled autonomous systems, (4) automatically find edge cases, (5) debug and root-cause failures of AI components including for perception, and (6) bridge the sim-to-real gap in autonomous system design. We will provide a hands-on tutorial on the basics of Scenic and its applications, how to create Scenic programs and your own new applications on top of Scenic, and to interface the language to your simulator/renderer of choice. For more information on Scenic, please visit the website:

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