Source author record

Abhishek Anand

Abhishek Anand appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record

Computer Vision Robotics Artificial Intelligence Computation and Language cond-mat.str-el

Catalog footprint

What is connected

5works

5topics

4close collaborators

Actions

Connect this record

Open graph Browse works

Inspect adjacent papers, topics, institutions and collaborators without losing the researcher page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

preprint2026arXiv

MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware

The recent advancement of Vision Language Action (VLA) models has driven a critical demand for large scale egocentric datasets. However, existing datasets are often limited by short episode durations, typically spanning only a few minutes, which fails to capture the long horizon temporal dependencies necessary for complex robotic task execution. To bridge this gap, we present MobileEgo Anywhere, a framework designed to facilitate the collection of robust, hour plus egocentric trajectories using commodity mobile hardware. We leverage the ubiquitous sensor suites of modern smartphones to provide high fidelity, long term camera pose tracking, effectively removing the high hardware barriers associated with traditional robotics data collection. Our contributions are three fold: (1) we release a novel dataset comprising 200 hours of diverse, long form egocentric data with persistent state tracking; (2) we open source our whole video processing infrastructure - STERA - that enables any user to record and process egocentric data, and (3) we provide a comprehensive processing pipeline to convert raw mobile captures into standardized, training ready formats for Vision Language Action model and foundation model research. By democratizing the data collection process, this work enables the massive scale acquisition of long horizon data across varied global environments, accelerating the development of generalizable robotic policies. Dataset and code can be accessed from https://www.fpvlabs.ai/stera

preprint2021arXiv

An Exactly Solvable Model for Strongly Interacting Electrons in a Magnetic Field

States of strongly interacting particles are of fundamental interest in physics, and can produce exotic emergent phenomena and topological structures. We consider here two-dimensional electrons in a magnetic field, and, departing from the standard practice of restricting to the lowest LL, introduce a model short-range interaction that is infinitely strong compared to the cyclotron energy. We demonstrate that this model lends itself to an exact solution for the ground as well as excited states at arbitrary filling factors $ν<1/2p$ and produces a fractional quantum Hall effect at fractions of the form $ν=n/(2pn+ 1)$, where n and p are integers. The fractional quantum Hall states of our model share many topological properties with the corresponding Coulomb ground states in the lowest Landau level, such as the edge physics and the fractional charge of the excitations.

preprint2012arXiv

3D Scene Grammar for Parsing RGB-D Pointclouds

We pose 3D scene-understanding as a problem of parsing in a grammar. A grammar helps us capture the compositional structure of real-word objects, e.g., a chair is composed of a seat, a back-rest and some legs. Having multiple rules for an object helps us capture structural variations in objects, e.g., a chair can optionally also have arm-rests. Finally, having rules to capture composition at different levels helps us formulate the entire scene-processing pipeline as a single problem of finding most likely parse-tree---small segments combine to form parts of objects, parts to objects and objects to a scene. We attach a generative probability model to our grammar by having a feature-dependent probability function for every rule. We evaluated it by extracting labels for every segment and comparing the results with the state-of-the-art segment-labeling algorithm. Our algorithm was outperformed by the state-or-the-art method. But, Our model can be trained very efficiently (within seconds), and it scales only linearly in with the number of rules in the grammar. Also, we think that this is an important problem for the 3D vision community. So, we are releasing our dataset and related code.

preprint2012arXiv

Contextually Guided Semantic Labeling and Search for 3D Point Clouds

RGB-D cameras, which give an RGB image to- gether with depths, are becoming increasingly popular for robotic perception. In this paper, we address the task of detecting commonly found objects in the 3D point cloud of indoor scenes obtained from such cameras. Our method uses a graphical model that captures various features and contextual relations, including the local visual appearance and shape cues, object co-occurence relationships and geometric relationships. With a large number of object classes and relations, the model's parsimony becomes important and we address that by using multiple types of edge potentials. We train the model using a maximum-margin learning approach. In our experiments over a total of 52 3D scenes of homes and offices (composed from about 550 views), we get a performance of 84.06% and 73.38% in labeling office and home scenes respectively for 17 object classes each. We also present a method for a robot to search for an object using the learned model and the contextual information available from the current labelings of the scene. We applied this algorithm successfully on a mobile robot for the task of finding 12 object classes in 10 different offices and achieved a precision of 97.56% with 78.43% recall.

preprint2011arXiv

Labeling 3D scenes for Personal Assistant Robots

Inexpensive RGB-D cameras that give an RGB image together with depth data have become widely available. We use this data to build 3D point clouds of a full scene. In this paper, we address the task of labeling objects in this 3D point cloud of a complete indoor scene such as an office. We propose a graphical model that captures various features and contextual relations, including the local visual appearance and shape cues, object co-occurrence relationships and geometric relationships. With a large number of object classes and relations, the model's parsimony becomes important and we address that by using multiple types of edge potentials. The model admits efficient approximate inference, and we train it using a maximum-margin learning approach. In our experiments over a total of 52 3D scenes of homes and offices (composed from about 550 views, having 2495 segments labeled with 27 object classes), we get a performance of 84.06% in labeling 17 object classes for offices, and 73.38% in labeling 17 object classes for home scenes. Finally, we applied these algorithms successfully on a mobile robot for the task of finding an object in a large cluttered room.