DeepNav: Learning to Navigate Large Cities

We present DeepNav, a Convolutional Neural Network (CNN) based algorithm for navigating large cities using locally visible street-view images. The DeepNav agent learns to reach its destination quickly by making the correct navigation decisions at intersections. We collect a large-scale dataset of street-view images organized in a graph where nodes are connected by roads. This dataset contains 10 city graphs and more than 1 million street-view images. We propose 3 supervised learning approaches for the navigation task and show how A* search in the city graph can be used to generate supervision for the learning. Our annotation process is fully automated using publicly available mapping services and requires no human input. We evaluate the proposed DeepNav models on 4 held-out cities for navigating to 5 different types of destinations. Our algorithms outperform previous work that uses hand-crafted features and Support Vector Regression (SVR)[19].Comment: CVPR 2017 camera ready versio

Empowering Latina/o Families to Navigate College Access

Background With the education crisis of Latinas/os, it is important to understand ways to increase access to college for the most vulnerable youth (Gándara & Contreras, 2009). To investigate strengths that promote college accessibility in underserved Latina/o families, the current qualitative study assessed the following: (1) Prior to beginning the intervention program and after the intervention program what forms of capital did families possess? (2) How did participating in the program change adolescents’ perception of their parents’ capital? (3) How did adolescents use agency to apply what they learned in college information intervention over time? Methods Latina/o parent-adolescent dyads (N = 11) participated in a college knowledge program in California. Sample included girls (67%) and 11-16 years of age (M = 14.0, SD = 1.78). Five of 11 families were interviewed 6-months post-effects of the intervention program. Research study used grounded theory inductive analysis approach (Corbin & Strauss, 2015). Results Question 1, Latina/o adolescents expressed aspirational, familial, navigational, and social capital before and after the intervention. Question 2, After participating in the intervention, adolescents expressed familial support through a combination of tangible (drop me off at school) and intangible (want me to be a good man) acts. Question 3, focused on the 6-month post-effects of the intervention program. Adolescents expressed agency by actively meeting requirements to apply to college, and understanding the path they need to reach their educational goals. Conclusion Discussion will focus on the importance of college information intervention programs in increasing Latino/a youth’s education experiences.https://scholarscompass.vcu.edu/gradposters/1107/thumbnail.jp

Localization in orchards using Extended Kalman Filter for sensor-fusion - A FroboMind component

Making an automated vehicle navigate in rows of orchards is a feature, relevant for automating the plant nursing and cultivation of the trees. To be able to navigate accurate and reliably, the vehicle must know its position relative to the trees in the orchards

Learning to Navigate Cloth using Haptics

We present a controller that allows an arm-like manipulator to navigate deformable cloth garments in simulation through the use of haptic information. The main challenge of such a controller is to avoid getting tangled in, tearing or punching through the deforming cloth. Our controller aggregates force information from a number of haptic-sensing spheres all along the manipulator for guidance. Based on haptic forces, each individual sphere updates its target location, and the conflicts that arise between this set of desired positions is resolved by solving an inverse kinematic problem with constraints. Reinforcement learning is used to train the controller for a single haptic-sensing sphere, where a training run is terminated (and thus penalized) when large forces are detected due to contact between the sphere and a simplified model of the cloth. In simulation, we demonstrate successful navigation of a robotic arm through a variety of garments, including an isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two baseline controllers: one without haptics and another that was trained based on large forces between the sphere and cloth, but without early termination.Comment: Supplementary video available at https://youtu.be/iHqwZPKVd4A. Related publications http://www.cc.gatech.edu/~karenliu/Robotic_dressing.htm

How can philosophy of language help us navigate the political news cycle?

In this chapter, I try to answer the above question, and another question that it presupposes: can philosophy of language help us navigate the political news cycle? A reader can be sceptical of a positive answer to the latter question; after all, citizens, political theorists, and journalists seem to be capable of following current politics and its coverage in the news, and there is no reason to think that philosophy of language in particular should be capable of helping people make sense and respond to the news. I will illustrate the application of philosophy of language to three contrasting strategies of political propaganda: dogwhistles, meaning perversions, and bald-faced lies. I hope that these help us see that philosophy of language can be a good tool in diagnosing demagoguery, and in resisting it