Perception–Affordance–Action: The Case of Agricultural Robots and Vehicles

The perception–affordance–action loop constitutes a fundamental framework for enabling advanced capabilities in semi-autonomous and autonomous robots. By allowing for robots to perceive their environment, interpret actionable possibilities (affordances), and execute appropriate actions, this loop ensures reliable, precise, and safe operation. This paradigm is particularly crucial in complex and dynamic domains such as agriculture. Semi-autonomous vehicles and robots are increasingly deployed across industries, including manufacturing, agriculture, healthcare, and services, where they perform tasks such as picking, grasping, processing, transporting materials, and interacting with living organisms such as animals, plants, and humans. As robots gain higher levels of autonomy, the need for sophisticated perception and affordance modeling becomes more critical. This article explores how the perception–affordance–action loop underpins these capabilities, and presents an experimental case study using a dual-arm robot platform, highlighting current limitations and future directions for intelligent robotic systems.