Policy-Based Planning for Robust Robot Navigation

This thesis proposes techniques for constructing and implementing an extensible navigation framework suitable for operating alongside or in place of traditional navigation systems. Robot navigation is only possible when many subsystems work in tandem such as localization and mapping, motion planning, control, and object tracking. Errors in any one of these subsystems can result in the robot failing to accomplish its task, oftentimes requiring human interventions that diminish the benefits theoretically provided by autonomous robotic systems. Our first contribution is Direction Approximation through Random Trials (DART), a method for generating human-followable navigation instructions optimized for followability instead of traditional metrics such as path length. We show how this strategy can be extended to robot navigation planning, allowing the robot to compute the sequence of control policies and switching conditions maximizing the likelihood with which the robot will reach its goal. This technique allows robots to select plans based on reliability in addition to efficiency, avoiding error-prone actions or areas of the environment. We also show how DART can be used to build compact, topological maps of its environments, offering opportunities to scale to larger environments. DART depends on the existence of a set of behaviors and switching conditions describing ways the robot can move through an environment. In the remainder of this thesis, we present methods for learning these behaviors and conditions in indoor environments. To support landmark-based navigation, we show how to train a Convolutional Neural Network (CNN) to distinguish between semantically labeled 2D occupancy grids generated from LIDAR data. By providing the robot the ability to recognize specific classes of places based on human labels, not only do we support transitioning between control laws, but also provide hooks for human-aided instruction and direction. Additionally, we suggest a subset of behaviors that provide DART with a sufficient set of actions to navigate in most indoor environments and introduce a method to learn these behaviors from teleloperated demonstrations. Our method learns a cost function suitable for integration into gradient-based control schemes. This enables the robot to execute behaviors in the absence of global knowledge. We present results demonstrating these behaviors working in several environments with varied structure, indicating that they generalize well to new environments. This work was motivated by the weaknesses and brittleness of many state-of-the-art navigation systems. Reliable navigation is the foundation of any mobile robotic system. It provides access to larger work spaces and enables a wide variety of tasks. Even though navigation systems have continued to improve, catastrophic failures can still occur (e.g. due to an incorrect loop closure) that limit their reliability. Furthermore, as work areas approach the scale of kilometers, constructing and operating on precise localization maps becomes expensive. These limitations prevent large scale deployments of robots outside of controlled settings and laboratory environments. The work presented in this thesis is intended to augment or replace traditional navigation systems to mitigate concerns about scalability and reliability by considering the effects of navigation failures for particular actions. By considering these effects when evaluating the actions to take, our framework can adapt navigation strategies to best take advantage of the capabilities of the robot in a given environment. A natural output of our framework is a topological network of actions and switching conditions, providing compact representations of work areas suitable for fast, scalable planning.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144073/1/rgoeddel_1.pd

Toward understanding natural language directions

Speaking using unconstrained natural language is an intuitive and flexible way for humans to interact with robots. Understanding this kind of linguistic input is challenging because diverse words and phrases must be mapped into structures that the robot can understand, and elements in those structures must be grounded in an uncertain environment. We present a system that follows natural language directions by extracting a sequence of spatial description clauses from the linguistic input and then infers the most probable path through the environment given only information about the environmental geometry and detected visible objects. We use a probabilistic graphical model that factors into three key components. The first component grounds landmark phrases such as "the computers" in the perceptual frame of the robot by exploiting co-occurrence statistics from a database of tagged images such as Flickr. Second, a spatial reasoning component judges how well spatial relations such as "past the computers" describe a path. Finally, verb phrases such as "turn right" are modeled according to the amount of change in orientation in the path. Our system follows 60% of the directions in our corpus to within 15 meters of the true destination, significantly outperforming other approaches.United States. Office of Naval Research (MURI N00014-07-1-0749

Material agency and performative dynamics in the practices of media art

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThis dissertation identifies a strategy of artistic inquiry within contemporary media art practice. It applies the concept of material that acts in an agential capacity, generating performative acts. It argues that the emergent potentials of materials and their interconnectedness with the compositional layers of a work can facilitate modes of effecting change in the artistic system. Through the theoretical investigation of the production processes of physical structures and environments, the thesis focuses on the compositional dynamics within which materials actively perform. It examines how Lars Spuybroek’s architectural design method of Material Machines (2004), and both the tactile potential as well as tactical uses of materials as generators to the formtaking process, might describe an open and active artistic strategy for employing the experimental capacities of such materialization processes. Building on philosophical and conceptual arguments that trace concepts of agency (Bruno Latour’s Actant-Network theory) and enactment (Karen Barad’s concept of intra-acting), the thesis introduces the two installation works ANI_MATE (described as a performative pneumatic stage machine) and ON TRACK (described as a mechanic-robotic installation). These apply the introduced artistic strategies. The analyses of these two artworks traces the particular capacities of the materials involved (respectively, their elasticity or viscosity) to negotiate forces of physical movement, which effect the system to transiently or irreversibly transform. ANI_MATE is a machine that is artist-operated and that explores the relationship between liveanimation procedures and the transformability and flexibility of its material environment. In contrast, ON TRACK’s performative machine ecology removes human agency. The machines act autonomously, giving rise to chance in the artistic system and allowing agency to emerge from the dynamic interconnectivity between materials, parts, and processes, eventually producing an entropic scenario of spilling resources. The thesis concludes that, in the context of a post digital paradigm in-development, such artistic practice offers a new strategy for an emergent aesthetics within contemporary physical-digital performance

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

Intelligence Without Reason

Computers and Thought are the two categories that together define Artificial Intelligence as a discipline. It is generally accepted that work in Artificial Intelligence over the last thirty years has had a strong influence on aspects of computer architectures. In this paper we also make the converse claim; that the state of computer architecture has been a strong influence on our models of thought. The Von Neumann model of computation has lead Artificial Intelligence in particular directions. Intelligence in biological systems is completely different. Recent work in behavior-based Artificial Intelligenge has produced new models of intelligence that are much closer in spirit to biological systems. The non-Von Neumann computational models they use share many characteristics with biological computation

Learning to identify users and predict their destination in a robotic guidance application

International audienceUser guidance systems are relevant to various applications of the service robotics field, among which: smart GPS navigator, robotic guides for museum or shopping malls or robotic wheel chairs for disabled persons. Such a system aims at helping its user to reach its destination in a fairly complex environment. If we assume the system is used in a fixed environment by multiple users for multiple navigation task over the course of days or weeks, then it is possible to take advantage of the user routine: from the initial navigational choice, users can be identified and their goal can be predicted. As a result of these prediction, the guidance system can bring its user to its destination while requiring less interaction. This property is particularly relevant for assisting disabled person for whom interaction is a long and complex task. In this paper, we implement a user guidance system using a dynamic Bayesian model and a topological representation of the environment. This model is evaluated with respect to the quality of its action prediction in a scenario involving 4 human users, and it is shown that in addition to the user identity, the goals and actions of the user are accurately predicted