Publications

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Reaching the limit in autonomous racing: Optimal control versus reinforcement learning

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Champion-level Drone Racing using Deep Reinforcement Learning

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LDM3D: Latent Diffusion Model for 3D

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Monocular Visual-Inertial Depth Estimation

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Zero-Shot Transfer of Haptics-based Object Insertion Policies

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Training Efficient Controllers via Analytic Policy Gradient

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Learning high-speed flight in the wild

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Training Graph Neural Networks with 1000 Layers

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OpenBot: Turning Smartphones into Robots

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DDA: Deep Drone Acrobatics

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SGAS: Sequential Greedy Architecture Search

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SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications

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DeepGCNs: Making GCNs Go as Deep as CNNs

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OIL: Observational Imitation Learning

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Learning a Controller Fusion Network by Online Trajectory Filtering for Vision-based UAV Racing

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TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild

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Driving Policy Transfer via Modularity and Abstraction

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Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications

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End-to-end Driving via Conditional Imitation Learning

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Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation

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Context-Aware Correlation Filter Tracking

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The Visual Object Tracking VOT2016 Challenge Results

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Target Response Adaptation for Correlation Filter Tracking

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Persistent Aerial Tracking System for UAVs

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A Benchmark and Simulator for UAV Tracking

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