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

FLAT2D: Fast localization from approximate transformation into 2D

Many autonomous vehicles require precise localization into a prior map in order to support planning and to leverage semantic information within those maps (e.g. that the right lane is a turn-only lane.) A popular approach in automotive systems is to use infrared intensity maps of the ground surface to localize, making them susceptible to failures when the surface is obscured by snow or when the road is repainted. An emerging alternative is to localize based on the 3D structure around the vehicle; these methods are robust to these types of changes, but the maps are costly both in terms of storage and the computational cost of matching. In this paper, we propose a fast method for localizing based on 3D structure around the vehicle using a 2D representation. This representation retains many of the advantages of "full" matching in 3D, but comes with dramatically lower space and computational requirements. We also introduce a variation of Graph-SLAM tailored to support localization, allowing us to make use of graph-based error-recovery techniques in our localization estimate. Finally, we present real-world localization results for both an indoor mobile robotic platform and an autonomous golf cart, demonstrating that autonomous vehicles do not need full 3D matching to accurately localize in the environment

The IκB Function of NF-κB2 p100 Controls Stimulated Osteoclastogenesis

The prototranscription factor p100 represents an intersection of the NF-κB and IκB families, potentially serving as both the precursor for the active NF-κB subunit p52 and as an IκB capable of retaining NF-κB in the cytoplasm. NF-κB–inducing kinase (NIK) controls processing of p100 to generate p52, and thus NIK-deficient mice can be used to examine the biological effects of a failure in such processing. We demonstrate that treatment of wild-type osteoclast precursors with the osteoclastogenic cytokine receptor activator of NF-κB ligand (RANKL) increases both expression of p100 and its conversion to p52, resulting in unchanged net levels of p100. In the absence of NIK, p100 expression is increased by RANKL, but its conversion to p52 is blocked, leading to cytosolic accumulation of p100, which, acting as an IκB protein, binds NF-κB complexes and prevents their nuclear translocation. High levels of unprocessed p100 in osteoclast precursors from NIK−/− mice or a nonprocessable form of the protein in wild-type cells impair RANKL-mediated osteoclastogenesis. Conversely, p100-deficient osteoclast precursors show enhanced sensitivity to RANKL. These data demonstrate a novel, biologically relevant means of regulating NF-κB signaling, with upstream control and kinetics distinct from the classical IκBα pathway

NF-κB-inducing kinase regulates selected gene expression in the Nod2 signaling pathway

The innate immune system surveys the extra- and intracellular environment for the presence of microbes. Among the intracellular sensors is a protein known as Nod2, a cytosolic protein containing a leucine-rich repeat domain. Nod2 is believed to play a role in determining host responses to invasive bacteria. A key element in upregulating host defense involves activation of the NF-κB pathway. It has been suggested through indirect studies that NF-κB-inducing kinase, or NIK, may be involved in Nod2 signaling. Here we have used macrophages derived from primary explants of bone marrow from wild-type mice and mice that either bear a mutation in NIK, rendering it inactive, or are derived from NIK(−/−) mice, in which the NIK gene has been deleted. We show that NIK binds to Nod2 and mediates induction of specific changes induced by the specific Nod2 activator, muramyl dipeptide, and that the role of NIK occurs in settings where both the Nod2 and TLR4 pathways are activated by their respective agonists. Specifically, we have linked NIK to the induction of the B-cell chemoattractant known as BLC and suggest that this chemokine may play a role in processes initiated by Nod2 activation that lead to improved host defense

NF-κB Antiapoptosis: Induction of TRAF1 and TRAF2 and c-IAP1 and c-IAP2 to Suppress Caspase-8 Activation

Tumor necrosis factor α (TNF-α) binding to the TNF receptor (TNFR) potentially initiates apoptosis and activates the transcription factor nuclear factor kappa B (NF-κB), which suppresses apoptosis by an unknown mechanism. The activation of NF-κB was found to block the activation of caspase-8. TRAF1 (TNFR-associated factor 1), TRAF2, and the inhibitor-of-apoptosis (IAP) proteins c-IAP1 and c-IAP2 were identified as gene targets of NF-κB transcriptional activity. In cells in which NF-κB was inactive, all of these proteins were required to fully suppress TNF-induced apoptosis, whereas c-IAP1 and c-IAP2 were sufficient to suppress etoposide-induced apoptosis. Thus, NF-κB activates a group of gene products that function cooperatively at the earliest checkpoint to suppress TNF-α–mediated apoptosis and that function more distally to suppress genotoxic agent–mediated apoptosis

Inferring Categories to Accelerate the Learning of New Classes

Abstract — On-the-fly learning systems are necessary for the deployment of general purpose robots. New training examples for such systems are often supplied by mentor interactions. Due to the cost of acquiring such examples, it is desirable to reduce the number of necessary interactions. Transfer learning has been shown to improve classification results for classes with small numbers of training examples by pooling knowledge from related classes. Standard practice in these works is to assume that the relationship between the transfer target and related classes is already known. In this work, we explore how previously learned categories, or related groupings of classes, can be used to transfer knowledge to novel classes without explicitly known relationships to them. We demonstrate an algorithm for determining the category membership of a novel class, focusing on the difficult case when few training examples are available. We show that classifiers trained via this method outperform classifiers optimized to learn the novel class individually when evaluated on both synthetic and real-world datasets. I

DART: A Particle-based Method for Generating Easy-to-Follow Directions

Abstract — Despite evidence that human wayfinders consider directions involving landmarks or topological descriptions easier to follow, the majority of commerical direction-planning services and GPS navigation units plan routes based on metrically or temporally shortest paths, ignoring this potentially valuable information. We propose a methodo for generating directions that maximizes the probability of a human arriving at the correct destination, taking into account a model of their ability to follow topological, metrical, and landmark-based directions. We discuss optimization techniques for employing these models and present a method, DART, for extracting model-improved sets of directions in a tractable amount of time. DART employs particle simulation techniques to maximize the probability that the modeled wayfinder will successfully reach their destination. Our synthetic evaluation shows that DART produces improvements in arrival rates over existing methods and illustrates how DART’s directions reflect properties of the wayfinder model. I