Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots This autonomous navigation system, developed for Robotic Systems Lab's wheeled-legged quadrupeds, is designed for efficient navigation in large urban environments. Using neural network policies, the unified control system supports smooth gait transitions, intelligent navigation planning, and responsive obstacle avoidance in urban settings. Video source: Robotic Systems Lab: Legged Robotics at ETH Zürich #robotics #tec
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Semantically-enhanced Deep Collision Prediction for Autonomous Navigation using Aerial Robots This work contributes a novel and modularized learning-based method for aerial robots navigating cluttered environments containing hard-to-perceive thin obstacles without assuming access to a map or the full pose estimation of the robot. The proposed solution builds upon a semantically-enhanced Variational Autoencoder that is trained with both real-world and simulated depth images to compress the input
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