Autonomous robots deployed in complex, natural human environments such as homes and offices need to manipulate numerous objects throughout their lifetimes. For an autonomous robot to operate effectively in such a setting and not require excessive training on part of a human operator, it should be capable of discovering how to reliably manipulate novel objects in the environment. We characterize the possible methods by which a robot can act on an object using the concept of affordances. Psychologist J.J. Gibson originally defined affordances as the action possibilities available in the environment to an agent. In the context of this work we define affordance-based behaviors as object manipulation strategies available to the robot, which correspond to specific semantic actions over which a task-level planner or end user of the robot can operate.
