Representing and learning affordance-based behaviors

Abstract

Autonomous robots deployed in complex, natural human environments such as homes and offices need to manipulate numerous objects throughout their deployment. For an autonomous robot to operate effectively in such a setting and not require excessive training from a human operator, it should be capable of discovering how to reliably manipulate novel objects it encounters. We characterize the possible methods by which a robot can act on an object using the concept of affordances. We define affordance-based behaviors as object manipulation strategies available to a robot, which correspond to specific semantic actions over which a task-level planner or end user of the robot can operate. This thesis concerns itself with developing the representation of these affordance- based behaviors along with associated learning algorithms. We identify three specific learning problems. The first asks which affordance-based behaviors a robot can successfully apply to a given object, including ones seen for the first time. Second, we examine how a robot can learn to best apply a specific behavior as a function of an object’s shape. Third, we investigate how learned affordance knowledge can be transferred between different objects and different behaviors. We claim that decomposing affordance-based behaviors into three separate factors— a control policy, a perceptual proxy, and a behavior primitive—aids an autonomous robot in learning to manipulate. Having a varied set of affordance-based behaviors available allows a robot to learn which behaviors perform most effectively as a function of an object’s identity or pose in the workspace. For a specific behavior a robot can use interactions with previously encountered objects to learn to robustly manipulate a novel object when first encountered. Finally, our factored representation allows a robot to transfer knowledge learned with one behavior to effectively manipulate an object in a qualitatively different manner by using a distinct controller or behavior primitive. We evaluate all work on a bimanual, mobile-manipulator robot. In all experiments the robot interacts with real-world objects sensed by an RGB-D camera.