In robotics, “affordance” refers to the actionable possibilities that objects or environments offer to a robot based on its capabilities. It’s a concept borrowed from psychology, where humans intuitively understand how to interact with objects (e.g., a chair affords sitting). For robots, affordances are explicitly modeled to enable decision-making about what actions are possible given sensory input. For example, a door handle’s shape and position might afford grasping and pulling, while a flat surface might afford placing objects. Affordances bridge perception (what the robot senses) and action (what it can do), allowing robots to reason about their surroundings without requiring exhaustive pre-programmed rules.
Robots use affordances by combining sensor data (e.g., cameras, lidar) with learned or programmed knowledge. A common approach involves training machine learning models to detect object features that correlate with specific actions. For instance, a robot might learn that cylindrical handles detected in a 3D scan are graspable, or that rectangular surfaces at hip height are likely tables for placing items. Developers often encode affordances using semantic labels (e.g., “pushable,” “movable”) paired with geometric constraints (e.g., object size, orientation). In mobile robotics, affordances like “navigable” might be derived from terrain slope or obstacle height. These models enable robots to generalize across environments—for example, recognizing that a lever in a factory and a lever in a home kitchen both afford pulling, despite differences in appearance.
Challenges in implementing affordances include handling variability in object shapes, materials, and environmental contexts. A cup’s affordance for holding liquid depends on its orientation—a detail robots must infer from vision or tactile feedback. Solutions often involve hierarchical representations: low-level affordances (e.g., “graspable region”) feed into higher-level task planning (e.g., “pour water”). Tools like convolutional neural networks (CNNs) for visual affordance detection or reinforcement learning for trial-and-error interaction are widely used. For developers, integrating affordances typically requires modular design—separating perception, affordance mapping, and action execution—to maintain flexibility. By focusing on what the robot can do rather than what objects are, affordances simplify complex environments into actionable decisions, making robots more adaptable and efficient.