Research papers

Duckietown is a versatile and accessible mobile robotics and embedded AI modular setup for reproducible scientific research.

Vision-based reinforcement learning for lane-tracking control

Vision-based Reinforcement Learning for Lane-Tracking Control

Deep Reinforcement Learning for Autonomous Navigation on Duckietown Platform: Evaluation of Adversarial Robustness

Evaluating Adversarial Robustness in Duckietown Navigation

Visual deep reinforcement learning paper snippet

Vision-Based DRL Autonomous Driving Agent with Sim2Real Transfer

Learning Skills to Navigate without a Master: A Sequential Multi-Policy Reinforcement Learning Algorithm snippet

Learning Skills to Navigate without a Master: A Sequential Multi-Policy Reinforcement Learning Algorithm

Monocular Robot Navigation with Self-Supervised Pretrained Vision Transformers

Automatic Wheels and Camera Calibration for Monocular and Differential Mobile Robots

Embedded out-of-distribution detection on an autonomous robot platform

Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously

Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents

A more comprehensive list of research papers is available on Google Scholar.

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