Representing and learning affordance-based behaviors

Author:
Description:
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 ...
Publisher:
Georgia Institute of Technology
Contributors:
Bobick, Aaron F. ; Rehg, James M. ; Christensen, Henrik I. ; Stilman, Mike ; Kemp, Charlie C. ; Fox, Dieter ; Interactive Computing
Year of Publication:
2014-05-22T15:27:16Z
Document Type:
Text ; Dissertation ; [Doctoral and postdoctoral thesis]
Language:
en_US
Subjects:
Robot learning ; Affordance learning ; Autonomous robots ; Machine learning
DDC:
004 Data processing & computer science (computed)
Relations:
URL:
http://hdl.handle.net/1853/51835
Content Provider:
Georgia Institute of Technology: SMARTech - Scholarly Materials and Research at Georgia Tech  Flag of United States of America