Learning What Can Be Picked: Active Reachability Estimation for Efficient Robotic Fruit Harvesting
Agriculture remains a cornerstone of global health and economic sustainability, yet labor-intensive tasks such as harvesting high-value crops continue to face growing workforce shortages. Robotic harvesting systems offer a promising solution; however, their deployment in unstructured orchard environments is constrained by inefficient perception-to-action pipelines. In particular, existing approaches often rely on exhaustive inverse kinematics or motion planning to determine whether a target fruit is reachable, leading to unnecessary computation and delayed decision-making. Our approach combines RGB-D perception with active learning to directly learn reachability as a binary decision probl...
| Authors | Nur Afsa Syeda, Mohamed Elmahallawy, Luis Fernando de la Torre et al. |
|---|---|
| Primary category | cs.RO |
| Published | 24 March 2026 |
| Last updated at source | 12 May 2026 |
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