Naxalbari at its Golden Jubilee: Fifty recent books on the Maoist movement in India

There are not many other issues in South Asia that have attracted as much scholarly attention in the last decade as India's Naxalite or Maoist movement. At least 50 scholarly or political books, several novels, and numerous essays have been published since 2007. What we hope to do in this article is to ask why this movement has generated such attention at this moment in time, to analyse the commentaries that have emerged and the questions that have been asked, and also to identify some of the shortfalls in the existing literature and propose some lines of research to be pursued by future scholars

Remarks on Differential Inclusion limits of Stochastic Approximation

For stochastic approximation algorithms with discontinuous dynamics, it is shown that under suitable distributional assumptions, the interpolated iterates track a Fillipov solution of the limiting differential inclusion. In addition, we give an alternative control theoretic approach to recent results of [7] on certain limiting empirical measures associated with the iteration

The Foundation Model Path to Open-World Robots

Data-driven robotics has been a very effective paradigm in the last decade. Today, we can can autonomously perform dexterous tasks like folding cloths, navigate tight hallways while avoiding collisions, and control complex dynamical systems like a quadrupedal robot walking across challenging terrains using onboard observations. But they often pose fundamental limitations that prevent them from being deployed in open-world environments, either because they make strong assumptions about the structure of their environment, require large amounts of on-robot data collection, or fail to account for semantic understanding of their surroundings. Due to these limitations, data-driven robotics approaches are still limited to simple restricted settings and not accessible to a majority of practitioners and potential applications. They still need to be hand-engineered for each separate robot, in a specific environment, to solve a specific task.This dissertation proposes an alternate vision for intelligent robots of the future, where we can have general machine learning models that can control any robot out of the box to perform reasonable behaviors in challenging open-world environments. Inspired by the onset of foundation models of language and vision, we present a recipe for training Robot Foundation Models (RFMs) from large amounts of data, collected across different environments and embodiments, that can control a wide variety of different mobile robots by only relying on egocentric vision. We also demonstrate how such an RFM can serve as a backbone for building very capable robotic systems, that can explore dense forests, or interact with humans in their environments, or utilize sources of side information such as satellite imagery or natural language. Finally, we propose a recipe for combining RFMs, with their knowledge of the physical world, with internet foundation models of language and vision, with their image-level semantic understanding and text-based reasoning, using a novel planning framework. This enables robotic systems to leverage the strength of internet foundation models, while also being grounded in real-world affordances and act in the real-world. We hope that this is a step towards such general purpose robotic systems that can be deployed on a wide range of robots, leverage internet-scale knowledge from pre-trained models, and serve as a foundation for diverse mobile robotic applications

Euthansia & personal autonomy rights for the terminally ill

Submitted in partial fulfillment of the requirements of the Bachelor of Laws Degree, Strathmore University Law SchoolThe objective of this dissertation is to determine the rights of the terminally ill with respect to euthanasia. There are currently no laws which provide for the terminally ill, leaving them to suffer in silence. This dissertation is limited to the rights of terminally ill persons. Further it argues for voluntary euthanasia and more specifically; voluntary passive euthanasia. In conducting this research, the laws of various different states are considered. However, the focus is the Kenyan law on these matters. The primary method is research used in this dissertation is desktop research. Consulting various sources such as textbooks, legislations and online resources. It was found that the laws in Kenya do not make any provisions for euthanasia or the rights of the terminally ill. The law does not permit euthanasia, however, it also does not prohibit it expressly. Since the right to life is not absolute, there is room for interpretation and exceptions to be made. The research found that if a hardline approach is taken with the right to life, it will curtail the rights of the terminally ill. Therefore it would be better to avoid such strong approaches in interpretations. The recommendation is to make provisions for the rights of the terminally ill. To stop their pain and suffering by providing them with a means to escape their pain and suffering should they choose to do so. It is also recommended that the state puts in place systems to ensure that any such rights given to the terminally ill are not abused. This should balance the rights of the terminally ill as well as the concern of those who strongly oppose it

Machine Vision Using Cellphone Camera: A Comparison of deep networks for classifying three challenging denominations of Indian Coins

Indian currency coins come in a variety of denominations. Off all the varieties Rs.1, RS.2, and Rs.5 have similar diameters. Majority of the coin styles in market circulation for denominations of Rs.1 and Rs.2 coins are nearly the same except for numerals on its reverse side. If a coin is resting on its obverse side, the correct denomination is not distinguishable by humans. Therefore, it was hypothesized that a digital image of a coin resting on its either size could be classified into its correct denomination by training a deep neural network model. The digital images were generated by using cheap cell phone cameras. To find the most suitable deep neural network architecture, four were selected based on the preliminary analysis carried out for comparison. The results confirm that two of the four deep neural network models can classify the correct denomination from either side of a coin with an accuracy of 97%.Comment: 6 Pages, 4 Figures, 6 Tables, Conference pape

SACSoN: Scalable Autonomous Data Collection for Social Navigation

Machine learning provides a powerful tool for building socially compliant robotic systems that go beyond simple predictive models of human behavior. By observing and understanding human interactions from past experiences, learning can enable effective social navigation behaviors directly from data. However, collecting navigation data in human-occupied environments may require teleoperation or continuous monitoring, making the process prohibitively expensive to scale. In this paper, we present a scalable data collection system for vision-based navigation, SACSoN, that can autonomously navigate around pedestrians in challenging real-world environments while encouraging rich interactions. SACSoN uses visual observations to observe and react to humans in its vicinity. It couples this visual understanding with continual learning and an autonomous collision recovery system that limits the involvement of a human operator, allowing for better dataset scaling. We use a this system to collect the SACSoN dataset, the largest-of-its-kind visual navigation dataset of autonomous robots operating in human-occupied spaces, spanning over 75 hours and 4000 rich interactions with humans. Our experiments show that collecting data with a novel objective that encourages interactions, leads to significant improvements in downstream tasks such as inferring pedestrian dynamics and learning socially compliant navigation behaviors. We make videos of our autonomous data collection system and the SACSoN dataset publicly available on our project page.Comment: 9 pages, 12 figures, 4 table

Rapid Exploration for Open-World Navigation with Latent Goal Models

We describe a robotic learning system for autonomous exploration and navigation in diverse, open-world environments. At the core of our method is a learned latent variable model of distances and actions, along with a non-parametric topological memory of images. We use an information bottleneck to regularize the learned policy, giving us (i) a compact visual representation of goals, (ii) improved generalization capabilities, and (iii) a mechanism for sampling feasible goals for exploration. Trained on a large offline dataset of prior experience, the model acquires a representation of visual goals that is robust to task-irrelevant distractors. We demonstrate our method on a mobile ground robot in open-world exploration scenarios. Given an image of a goal that is up to 80 meters away, our method leverages its representation to explore and discover the goal in under 20 minutes, even amidst previously-unseen obstacles and weather conditions. Please check out the project website for videos of our experiments and information about the real-world dataset used at https://sites.google.com/view/recon-robot.Comment: Accepted for presentation at 5th Annual Conference on Robot Learning (CoRL 2021), London, UK as an Oral Talk. Project page and dataset release at https://sites.google.com/view/recon-robo