Day 1 (in 290 Hearst Mining)
8:00-8:30: Breakfast
8:30-8:40: Welcome & Introductions
8:40-9:00: Project Overview [Sanjit Seshia]
Session 1: Human Modeling and Interaction
9:00-9:20: Human-machine collaboration and information processing limitations [Mark Ho]
9:20-9:40: Modeling human attention as optimal sequential sampling [Fred Callaway]
9:40-10:00: Learning Task Specifications from Demonstrations [Marcell Vazquez-Chanlatte]
10:00-10:20: Maximum Likelihood Constraint Inference from Demonstrations [Dexter Scobee]
10:20-10:30: Nonverbal Robot Feedback for Human Teachers [Isabella Huang]
10:30-11:00: Break
Session 2: Control and Verification
11:00-11:20: Safe Learning-Based Control [Claire Tomlin]
11:20-11:40: A Hamilton-Jacobi Reachability-Based Framework for Predicting and Analyzing Human Motion for Safe Planning [Andrea Bajcsy and Somil Bansal]
11:40-12:00: Counterexample-Guided Synthesis of Perception Models and Control [Hadi Ravanbakhsh]
12:00-12:20: Towards Assume-Guarantee Profiles for Autonomous Vehicles [Tung Phan and Karena Cai]
12:20-12:40: VerifAI: A Toolkit for the Formal Design and Analysis of AI-Based Systems [Daniel Fremont]
12:40-2:00: Lunch & poster session
Session 3: Human Interfaces, Education and Outreach
2:00-2:20: Education Efforts in AR/VR and Mobility [Bjoern Hartmann]
2:20-2:30: Girls in Engineering Summer Program [Lizzie Hager-Barnard]
2:30-2:50: High Resolution Soft Tactile Sensor for Physical Human-Robot Interaction [Isabella Huang]
2:50-3:00: Optimal Design of Human-Machine Interfaces [Mark Ho]
Session 4: Visitor/Alumni talks
3:00-3:20: RuleBooks for Autonomous Vehicles [Nok Wongpiromsarn (nuTonomy)]
3:20-3:40: Fly-by-Logic: Trajectory Generation for Drone Fleets with Temporal Logic Objectives [Yash Pant (UPenn, new VeHICaL postdoc)]
3:40-4:00: Analyzing the Boeing 737 Max Incidents [Jaime Fernandez Fisac, alumnus]
4:00-4:20: Break
Final Session
4:20-5:00: Discussion & Feedback, plans for Day 2
Day 2 (in 540 Cory Hall)
9:00-10:30: Breakout sessions on the following topics:
- Human Modeling and Human-Machine Interaction
- Requirements and Causality
- Verified Machine Learning for Perception and Autonomy