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Abstract — In this paper, we propose that a robot can create a series of monitors describing its own pointing gesture and reaching behavior and that these models can be used to infer human intention delivered by a pointing gesture and assist accordingly. This extends our previous work where we demonstrated that an intrinsically motivated robot can be designed to seek controllable relationships with the world and employ them to solicit assistance from a nearby human via expressive gestures. Preliminary experimental results demonstrate that our approach enables a robot to respond appropriately after learning a receptive quality of gesture. I.

Abstract—Autonomous robots demand complex behavior to perform tasks in unstructured environments. In order to meet these expectations efficiently, it is necessary to organize knowledge of past interactions with the world in order to facilitate future tasks. With this goal in mind, we present a knowledge representation that makes explicit the invariant spatial relationships between sensorimotor features comprising a rigid body and uses them to reason about other tasks and run-time contexts. I.

Autonomous robots demand complex behavior to deal with unstructured environments. To meet these expectations, a robot needs to address a suite of problems associated with long term knowledge acquisition, representation, and execution in the presence of partial information. In this paper, we address these issues by the acquisition of broad, domain general skills using an intrinsically motivated reward function. We show how these skills can be represented compactly and used hierarchically to obtain complex manipulation skills. We further present a Bayesian model using the learned skills to model objects in the world, in terms of the actions they afford. We argue that our knowledge representation allows a robot to both predict the dynamics of objects in the world as well as recognize them. 1

This dissertation presents an approach to robot programming by demonstration based on two key concepts: demonstrator intent is the most meaningful signal that the robot can observe, and the robot should have a basic level of behavioral competency from which to interpret observed actions. Intent is a teleological, robust teaching signal invariant to many common sources of noise in training. The robot can use the knowledge encapsulated in sensorimotor schemas to interpret the demonstration. Furthermore, knowledge gained in prior demonstrations can be applied to future sessions. iv I argue that programming by demonstration be organized into declarative and procedural components. The declarative component represents a reusable outline of underlying behavior that can be applied to many different contexts. The procedural component represents the dynamic portion of the task that is based on features observed at run time. I describe how statistical models, and Bayesian methods in particular, can be used to model these components. These models have many features that are beneficial for learning in this domain, such as tolerance for uncertainty, and the ability to incorporate prior knowledge into inferences. I demonstrate this architecture through experiments on a bimanual humanoid robot using tasks from the pick and place domain.

Abstract. Humans and animals acquire their wide repertoire of motor skills through an incremental learning process, during which progressively more complex skills are acquired and subsequently integrated with prior abilities. The order in which the skills are learned and the progressive manner in which they are developed play an important role in developing a final skill set. Inspired by this general idea, we develop an approach for learning motor skills based on a two-level curriculum. At the high level, the curriculum specifies an order in which different skills should be learned. At the low level, the curriculum defines a process for learning within a skill. The method is used to develop an ensemble of highly dynamic integrated motor skills for a planar articulated figure capable of doing parameterized hops, flips, rolls, and acrobatic sequences. Importantly, we demonstrate that the same curriculum can be successfully applied to significant variations of the articulated figure to yield appropriately individualized motor skill sets. 1