Learning Probabilistic Discriminative Models of Grasp Affordances under Limited Supervision

peer reviewe

Behavior-grounded multi-sensory object perception and exploration by a humanoid robot

Infants use exploratory behaviors to learn about the objects around them. Psychologists have theorized that behaviors such as touching, pressing, lifting, and dropping enable infants to form grounded object representations. For example, scratching an object can provide information about its roughness, while lifting it can provide information about its weight. In a sense, the exploratory behavior acts as a ``question\u27\u27 to the object, which is subsequently ``answered by the sensory stimuli produced during the execution of the behavior. In contrast, most object representations used by robots today rely solely on computer vision or laser scan data, gathered through passive observation. Such disembodied approaches to robotic perception may be useful for recognizing an object using a 3D model database, but nevertheless, will fail to infer object properties that cannot be detected using vision alone. To bridge this gap, this dissertation introduces a framework for object perception and exploration in which the robot\u27s representation of objects is grounded in its own sensorimotor experience with them. In this framework, an object is represented by sensorimotor contingencies that span a diverse set of exploratory behaviors and sensory modalities. The results from several large-scale experimental studies show that the behavior-grounded object representation enables a robot to solve a wide variety of tasks including recognition of objects based on the stimuli that they produce, object grouping and sorting, and learning category labels that describe objects and their properties