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Mark Palatucci

2 accepted papers

2022

Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving

IROS 2022poster

We demonstrate the first large-scale application of model-based generative adversarial imitation learning (MGAIL) to the task of dense urban self-driving. We augment standard MGAIL using a hierarchical model to enable generalization to arbitrary goal routes, and measure performance using a closed-lo…

Cited by 60SourceScholar
2022

Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation

ICRA 2022poster

Simulation is a crucial tool for accelerating the development of autonomous vehicles. Making simulation realistic requires models of the human road users who interact with such cars. Such models can be obtained by applying learning from demonstration (LfD) to trajectories observed by cars already on…

Cited by 69SourceScholar