25 citations · 39 across the 5 of their papers we have counts for
6 papers
CaliBench: Are the Stochastic Dynamics of Video World Models Physically Calibrated?
Jonathan Sadeghi, Jenny Seidenschwarz, Jesse Allardice +3
Video world models approximate the stochastic distribution of physical outcomes through generative sampling, but existing benchmarks score individual generations or compare distrib…
Hierarchical Imitation Learning for Stochastic Environments
Maximilian Igl, Punit Shah, Paul Mougin +5
Many applications of imitation learning require the agent to generate the full distribution of behaviour observed in the training data. For example, to evaluate the safety of auton…
Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula
Eli Bronstein, Sirish Srinivasan, Supratik Paul +4
ML-based motion planning is a promising approach to produce agents that exhibit complex behaviors, and automatically adapt to novel environments. In the context of autonomous drivi…
Posetal Games: Efficiency, Existence, and Refinement of Equilibria in Games with Prioritized Metrics
Alessandro Zanardi, Gioele Zardini, Sirish Srinivasan +4
Modern applications require robots to comply with multiple, often conflicting rules and to interact with the other agents. We present Posetal Games as a class of games in which eac…
A Holistic Motion Planning and Control Solution to Challenge a Professional Racecar Driver
Sirish Srinivasan, Sebastian Nicolas Giles, Alexander Liniger
We present a holistically designed three layer control architecture capable of outperforming a professional driver racing the same car. Our approach focuses on the co-design of the…
End-to-End Velocity Estimation For Autonomous Racing
Sirish Srinivasan, Inkyu Sa, Alex Zyner +3
Velocity estimation plays a central role in driverless vehicles, but standard and affordable methods struggle to cope with extreme scenarios like aggressive maneuvers due to the pr…