Learning What Can Be Picked: Active Reachability Estimation for Efficient Robotic Fruit Harvesting

Learning What Can Be Picked: Active Reachability Estimation for Efficient Robotic Fruit Harvesting explores A robotic harvesting system that uses active learning to efficiently determine fruit reachability, reducing computational load and annotation effort for deployment in unstructured orchards.. Commercial viability score: 7/10 in Robotics.