Reinforcement Learning for Active Perception in Autonomous Navigation. [📍GitHub & Paper ] Most robots navigate as if their cameras were nailed in place. But perception is not passive. Animals move their heads and eyes constantly to decide where to go next. Robots should do the same. That is the idea behind “Reinforcement Learning for Active Perception in Autonomous Navigation,” which has just been accepted at ICRA 2026. Instead of treating navigation and perception as two separate problems, this work trains flying robots to do both at once. The robot does not only decide where to move. It also decides where to look. Using reinforcement learning, the robot learns to: •fly safely through cluttered environments, •actively reorient its onboard camera to reduce uncertainty, •balance reaching a goal with gathering better visual information. The key result is that actively controlling perception makes navigation safer. In simulation and on a real flying robot, this approach consistently outperforms static-camera baselines. The sim-to-real transfer holds up, which is usually where things break. This is a small but important shift in how we think about autonomy. Better planning does not always come from better maps or bigger models. Sometimes it comes from simply looking in the right direction at the right time. Thanks for sharing, Kostas Alexis! 📍Code: Paper: Video: —— Weekly robotics and AI insights. Subscribe free: