arXiv subjects

Ashwin Ashok

Publications and source records attributed to Ashwin Ashok.

13 recordsLinked to original sources

arXiv2026

FeudalNav: A Simple Framework for Visual Navigation

Visual navigation for robotics is inspired by the human ability to navigate environments using visual cues and memory, eliminating the need for detailed maps. In unseen, unmapped, or GPS-denied settings, traditional metric map-based methods fall short, prompting a shift toward learning-based approaches with minimal exploration. In this work, we develop a hierarchical framework that decomposes the navigation decision-making process into multiple levels. Our method learns to select subgoals through a simple, transferable waypoint selection network. A key component of the approach is a latent-space memory module organized solely by visual similarity, as a proxy for distance. This alternative to graph-based topological representations proves sufficient for navigation tasks, providing a compact, light-weight, simple-to-train navigator that can find its way to the goal in novel locations. We show competitive results with a suite of SOTA methods in Habitat AI environments without using any odometry in training or inference. An additional contribution leverages the interpretablility of the framework for interactive navigation. We consider the question: how much direction intervention/interaction is needed to achieve success in all trials? We demonstrate that even minimal human involvement can significantly enhance overall navigation performance.

cs.RO↗

arXiv2025

Non Line-of-Sight Optical Wireless Communication using Neuromorphic Cameras

Neuromorphic or event cameras, inspired by biological vision systems, capture changes in illumination with high temporal resolution and efficiency, producing streams of events rather than traditional images. In this paper, we explore the use of neuromorphic cameras for passive optical wireless communication (OWC), leveraging their asynchronous detection of illumination changes to decode data transmitted through reflections of light from objects. We propose a novel system that utilizes neuromorphic cameras for passive visible light communication (VLC), extending the concept to Non Line-of-Sight (NLoS) scenarios through passive reflections from everyday objects. Our experiments demonstrate the feasibility and advantages of using neuromorphic cameras for VLC, characterizing the performance of various modulation schemes, including traditional On-Off Keying (OOK) and advanced N-pulse modulation. We introduce an adaptive N-pulse modulation scheme that dynamically adjusts encoding based on the packet's bit composition, achieving higher data rates and robustness in different scenarios. Our results show that lighter-colored, glossy objects are better for NLoS communication, while larger objects and those with matte finishes experience higher error rates due to multipath reflections.

cs.CV↗

arXiv2025

Text2Net: Transforming Plain-text To A Dynamic Interactive Network Simulation Environment

This paper introduces Text2Net, an innovative text-based network simulation engine that leverages natural language processing (NLP) and large language models (LLMs) to transform plain-text descriptions of network topologies into dynamic, interactive simulations. Text2Net simplifies the process of configuring network simulations, eliminating the need for users to master vendor-specific syntaxes or navigate complex graphical interfaces. Through qualitative and quantitative evaluations, we demonstrate Text2Net's ability to significantly reduce the time and effort required to deploy network scenarios compared to traditional simulators like EVE-NG. By automating repetitive tasks and enabling intuitive interaction, Text2Net enhances accessibility for students, educators, and professionals. The system facilitates hands-on learning experiences for students that bridge the gap between theoretical knowledge and practical application. The results showcase its scalability across various network complexities, marking a significant step toward revolutionizing network education and professional use cases, such as proof-of-concept testing.

cs.NI↗

arXiv2025

Revelio: A Real-World Screen-Camera Communication System with Visually Imperceptible Data Embedding

We present `Revelio', a real-world screen-camera communication system leveraging temporal flicker fusion in the OKLAB color space. Using spatially-adaptive flickering and encoding information in pixel region shapes, Revelio achieves visually imperceptible data embedding while remaining robust against noise, asynchronicity, and distortions in screen-camera channels, ensuring reliable decoding by standard smartphone cameras. The decoder, driven by a two-stage neural network, uses a weighted differential accumulator for precise frame detection and symbol recognition. Initial experiments demonstrate Revelio's effectiveness in interactive television, offering an unobtrusive method for meta-information transmission.

cs.MM↗

arXiv2024

Memory Proxy Maps for Visual Navigation

Visual navigation takes inspiration from humans, who navigate in previously unseen environments using vision without detailed environment maps. Inspired by this, we introduce a novel no-RL, no-graph, no-odometry approach to visual navigation using feudal learning to build a three tiered agent. Key to our approach is a memory proxy map (MPM), an intermediate representation of the environment learned in a self-supervised manner by the high-level manager agent that serves as a simplified memory, approximating what the agent has seen. We demonstrate that recording observations in this learned latent space is an effective and efficient memory proxy that can remove the need for graphs and odometry in visual navigation tasks. For the mid-level manager agent, we develop a waypoint network (WayNet) that outputs intermediate subgoals, or waypoints, imitating human waypoint selection during local navigation. For the low-level worker agent, we learn a classifier over a discrete action space that avoids local obstacles and moves the agent towards the WayNet waypoint. The resulting feudal navigation network offers a novel approach with no RL, no graph, no odometry, and no metric map; all while achieving SOTA results on the image goal navigation task.

cs.CV↗

arXiv2024

A Landmark-Aware Visual Navigation Dataset

Map representations learned by expert demonstrations have shown promising research value. However, the field of visual navigation still faces challenges due to the lack of real-world human-navigation datasets that can support efficient, supervised, representation learning of environments. We present a Landmark-Aware Visual Navigation (LAVN) dataset to allow for supervised learning of human-centric exploration policies and map building. We collect RGBD observation and human point-click pairs as a human annotator explores virtual and real-world environments with the goal of full coverage exploration of the space. The human annotators also provide distinct landmark examples along each trajectory, which we intuit will simplify the task of map or graph building and localization. These human point-clicks serve as direct supervision for waypoint prediction when learning to explore in environments. Our dataset covers a wide spectrum of scenes, including rooms in indoor environments, as well as walkways outdoors. We release our dataset with detailed documentation at https://huggingface.co/datasets/visnavdataset/lavn (DOI: 10.57967/hf/2386) and a plan for long-term preservation.

cs.CV↗

arXiv2024

Feudal Networks for Visual Navigation

Visual navigation follows the intuition that humans can navigate without detailed maps. A common approach is interactive exploration while building a topological graph with images at nodes that can be used for planning. Recent variations learn from passive videos and can navigate using complex social and semantic cues. However, a significant number of training videos are needed, large graphs are utilized, and scenes are not unseen since odometry is utilized. We introduce a new approach to visual navigation using feudal learning, which employs a hierarchical structure consisting of a worker agent, a mid-level manager, and a high-level manager. Key to the feudal learning paradigm, agents at each level see a different aspect of the task and operate at different spatial and temporal scales. Two unique modules are developed in this framework. For the high-level manager, we learn a memory proxy map in a self supervised manner to record prior observations in a learned latent space and avoid the use of graphs and odometry. For the mid-level manager, we develop a waypoint network that outputs intermediate subgoals imitating human waypoint selection during local navigation. This waypoint network is pre-trained using a new, small set of teleoperation videos that we make publicly available, with training environments different from testing environments. The resulting feudal navigation network achieves near SOTA performance, while providing a novel no-RL, no-graph, no-odometry, no-metric map approach to the image goal navigation task.

cs.CV↗

arXiv2023

ViFiT: Reconstructing Vision Trajectories from IMU and Wi-Fi Fine Time Measurements

Tracking subjects in videos is one of the most widely used functions in camera-based IoT applications such as security surveillance, smart city traffic safety enhancement, vehicle to pedestrian communication and so on. In the computer vision domain, tracking is usually achieved by first detecting subjects with bounding boxes, then associating detected bounding boxes across video frames. For many IoT systems, images captured by cameras are usually sent over the network to be processed at a different site that has more powerful computing resources than edge devices. However, sending entire frames through the network causes significant bandwidth consumption that may exceed the system bandwidth constraints. To tackle this problem, we propose ViFiT, a transformer-based model that reconstructs vision bounding box trajectories from phone data (IMU and Fine Time Measurements). It leverages a transformer ability of better modeling long-term time series data. ViFiT is evaluated on Vi-Fi Dataset, a large-scale multimodal dataset in 5 diverse real world scenes, including indoor and outdoor environments. To fill the gap of proper metrics of jointly capturing the system characteristics of both tracking quality and video bandwidth reduction, we propose a novel evaluation framework dubbed Minimum Required Frames (MRF) and Minimum Required Frames Ratio (MRFR). ViFiT achieves an MRFR of 0.65 that outperforms the state-of-the-art approach for cross-modal reconstruction in LSTM Encoder-Decoder architecture X-Translator of 0.98, resulting in a high frame reduction rate as 97.76%.

cs.CV↗

arXiv2022

ViFiCon: Vision and Wireless Association Via Self-Supervised Contrastive Learning

We introduce ViFiCon, a self-supervised contrastive scheme which learns a cross-modal association between vision and wireless modalities. Specifically, the system uses pedestrian data collected from RGB-D camera footage and WiFi Fine Time Measurements (FTM) from a user's smartphone device. Depth data from RGB-D (vision domain) is inherently linked with an observable pedestrian, but FTM data (wireless domain) is associated only to a smartphone on the network. We represent temporal sequences from both vision and wireless domains by stacking multi-person depth data sequences within an image representation. This simplicity allows both scene-wide processing and fewer vision and wireless features, alleviating privacy and energy associated with transmitting IMU data. To facilitate self-supervised learning, we design a scene-wide synchronization pretext task for our network and then employ the learned representation for the downstream multimodal association task. We show that compared to fully supervised state-of-the-art models, ViFiCon achieves high performance vision-to-wireless association of 92.63% in 25 frames sliding window fashion (2.5s), finding which bounding box corresponds to which smartphone device, without hand-labeled association examples for training data. Extensive experimental results demonstrate ViFiCon applicability in real-world systems when wireless data annotations are scarce.

cs.CV↗

arXiv2021

DeepLight: Robust & Unobtrusive Real-time Screen-Camera Communication for Real-World Displays

The paper introduces a novel, holistic approach for robust Screen-Camera Communication (SCC), where video content on a screen is visually encoded in a human-imperceptible fashion and decoded by a camera capturing images of such screen content. We first show that state-of-the-art SCC techniques have two key limitations for in-the-wild deployment: (a) the decoding accuracy drops rapidly under even modest screen extraction errors from the captured images, and (b) they generate perceptible flickers on common refresh rate screens even with minimal modulation of pixel intensity. To overcome these challenges, we introduce DeepLight, a system that incorporates machine learning (ML) models in the decoding pipeline to achieve humanly-imperceptible, moderately high SCC rates under diverse real-world conditions. Deep-Light's key innovation is the design of a Deep Neural Network (DNN) based decoder that collectively decodes all the bits spatially encoded in a display frame, without attempting to precisely isolate the pixels associated with each encoded bit. In addition, DeepLight supports imperceptible encoding by selectively modulating the intensity of only the Blue channel, and provides reasonably accurate screen extraction (IoU values >= 83%) by using state-of-the-art object detection DNN pipelines. We show that a fully functional DeepLight system is able to robustly achieve high decoding accuracy (frame error rate < 0.2) and moderately-high data goodput (>=0.95Kbps) using a human-held smartphone camera, even over larger screen-camera distances (approx =2m).

cs.CV↗

arXiv2019

Multiple Access Visible Light Communication using a Single Photo-Receptor

The directionality of optical signals provides an opportunity for efficient space reuse of optical links in visible light communication (VLC). Space reuse in VLC can enable multiple access communication from multiple light emitting transmitters. However, traditional VLC system design using photo-receptors requires at least one receiving photodetector element for each light emitter, thus constraining VLC to always require a light emitter-to-light receiving element pair. In this paper, we propose, design and evaluate a novel architecture for VLC that can enable multiple-access reception using a photoreceptor receiver that uses only a single photodiode. The novel design includes a liquid-crystal-display (LCD) based shutter system that can be automated to control and enable selective reception of light beams from multiple transmitters. We evaluate the feasibility of multiple access on a single photodiode from two light emitting diode (LED) transmitters and the performance of the communication link using bit-error-rate (BER) and packet-error-rate (PER) metrics. Our experiment and trace based evaluation reveals the feasibility of multiple LED reception on a single photodiode and estimated throughput of the order of Mbps.

eess.SP↗

arXiv2019

A Data Set of Internet Claims and Comparison of their Sentiments with Credibility

In this modern era, communication has become faster and easier. This means fallacious information can spread as fast as reality. Considering the damage that fake news kindles on the psychology of people and the fact that such news proliferates faster than truth, we need to study the phenomenon that helps spread fake news. An unbiased data set that depends on reality for rating news is necessary to construct predictive models for its classification. This paper describes the methodology to create such a data set. We collect our data from snopes.com which is a fact-checking organization. Furthermore, we intend to create this data set not only for classification of the news but also to find patterns that reason the intent behind misinformation. We also formally define an Internet Claim, its credibility, and the sentiment behind such a claim. We try to realize the relationship between the sentiment of a claim with its credibility. This relationship pours light on the bigger picture behind the propagation of misinformation. We pave the way for further research based on the methodology described in this paper to create the data set and usage of predictive modeling along with research-based on psychology/mentality of people to understand why fake news spreads much faster than reality.

cs.IR↗

arXiv2015

Optimal Radiometric Calibration for Camera-Display Communication

We present a novel method for communicating between a camera and display by embedding and recovering hidden and dynamic information within a displayed image. A handheld camera pointed at the display can receive not only the display image, but also the underlying message. These active scenes are fundamentally different from traditional passive scenes like QR codes because image formation is based on display emittance, not surface reflectance. Detecting and decoding the message requires careful photometric modeling for computational message recovery. Unlike standard watermarking and steganography methods that lie outside the domain of computer vision, our message recovery algorithm uses illumination to optically communicate hidden messages in real world scenes. The key innovation of our approach is an algorithm that performs simultaneous radiometric calibration and message recovery in one convex optimization problem. By modeling the photometry of the system using a camera-display transfer function (CDTF), we derive a physics-based kernel function for support vector machine classification. We demonstrate that our method of optimal online radiometric calibration (OORC) leads to an efficient and robust algorithm for computational messaging between nine commercial cameras and displays.

cs.CV↗