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

Researchers at ETH Zürich have taught ANYmal-D, a four-legged manipulator robot, to play badminton against humans. The system uses a reinforcement-learning controller that integrates vision, locomotion, and arm manipulation to intercept and return shots. While the robot isn’t yet at human-competitive level, the work demonstrates how sport can serve as a proxy task for developing robots with dynamic perception-motion coordination—a capability relevant for applications such as search and rescue or