Karaman, Sertac

16 publications

ICML 2025 Highly Compressed Tokenizer Can Generate Without Training Lukas Lao Beyer, Tianhong Li, Xinlei Chen, Sertac Karaman, Kaiming He

ICLR 2025 ReGen: Generative Robot Simulation via Inverse Design Phat Tan Nguyen, Tsun-Hsuan Wang, Zhang-Wei Hong, Erfan Aasi, Andrew Silva, Guy Rosman, Sertac Karaman, Daniela Rus

CoRL 2023 Dynamic Multi-Team Racing: Competitive Driving on 1/10-Th Scale Vehicles via Learning in Simulation Peter Werner, Tim Seyde, Paul Drews, Thomas Matrai Balch, Igor Gilitschenski, Wilko Schwarting, Guy Rosman, Sertac Karaman, Daniela Rus

L4DC 2022 Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks Ryan Sander, Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Sertac Karaman, Daniela Rus

CoRL 2022 Real-Time Generation of Time-Optimal Quadrotor Trajectories with Semi-Supervised Seq2Seq Learning Gilhyun Ryou, Ezra Tal, Sertac Karaman

L4DC 2021 Feedback from Pixels: Output Regulation via Learning-Based Scene View Synthesis Murad Abu-Khalaf, Sertac Karaman, Daniela Rus

CoRL 2021 Learning a Risk-Aware Trajectory Planner from Demonstrations Using Logic Monitor Xiao Li, Jonathan DeCastro, Cristian Ioan Vasile, Sertac Karaman, Daniela Rus

CoRL 2021 Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles Tim Seyde, Wilko Schwarting, Sertac Karaman, Daniela Rus

NeurIPSW 2021 Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks Ryan Sander, Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Sertac Karaman, Daniela Rus

CoRL 2020 Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Lucas Liebenwein, Ryan Sander, Sertac Karaman, Daniela Rus

ICLR 2020 Deep Orientation Uncertainty Learning Based on a Bingham Loss Igor Gilitschenski, Roshni Sahoo, Wilko Schwarting, Alexander Amini, Sertac Karaman, Daniela Rus

CoRL 2020 Differentiable Logic Layer for Rule Guided Trajectory Prediction Xiao Li, Guy Rosman, Igor Gilitschenski, Jonathan DeCastro, Cristian-Ioan Vasile, Sertac Karaman, Daniela Rus

L4DC 2020 Learning to Plan via Deep Optimistic Value Exploration Tim Seyde, Wilko Schwarting, Sertac Karaman, Daniela Rus

ICMLW 2019 Real-World Autonomous Vehicle Control Trained Entirely Within Data-Driven Simulation Alexander Amini, Igor Gilitschenski, Jacob Phillips, Julia Moseyko, Sertac Karaman, Daniela Rus

NeurIPS 2018 Invertibility of Convolutional Generative Networks from Partial Measurements Fangchang Ma, Ulas Ayaz, Sertac Karaman

AAAI 2017 Model AI Assignments 2017 Todd W. Neller, Joshua Eckroth, Sravana Reddy, Joshua Ziegler, Jason M. Bindewald, Gilbert L. Peterson, Thomas P. Way, Paula Matuszek, Lillian N. Cassel, Mary-Angela Papalaskari, Carol Weiss, Ariel Anders, Sertac Karaman