Publications

2026

1. paper

   Wiggle and Go! System Identification for Zero-Shot Dynamic Rope Manipulation

   Abhinav Mahajan, Arindam Sarkar, and Prakash Mandayam Comar

   2026

   Under Review

   Abs

   Many robotic tasks are unforgiving; a single mistake in a dynamic throw can lead to unacceptable delays or unrecoverable failure. To mitigate this, we present a novel approach that leverages learned simulation priors to inform goal-conditioned dynamic manipulation of ropes for efficient and accurate task execution. Related methods for dynamic rope manipulation either require large real-world datasets to estimate rope behavior or the use of iterative improvements on attempts at the task for goal completion. We introduce Wiggle and Go!, a system-identification, two-stage framework that enables zero-shot task rope manipulation. The framework consists of a system identification module that observes rope movement to predict descriptive physical parameters, which then informs an optimization method for goal-conditioned action prediction for the robot to execute zero-shot in the real. Our method achieves strong performance across multiple dynamic manipulation tasks enabled by the same task-agnostic system identification module which offers seamless switching between different manipulation tasks, allowing a single model to support a diverse array of manipulation policies. We achieve a 3.55 cm average accuracy on 3D target striking in real using rope system parameters in comparison to 15.34 cm accuracy when our task model is not system-parameter-informed. We achieve a Pearson correlation coefficient of 0.95 between Fourier frequencies of the predicted and real ropes on an unseen trajectory.

2. MERIT: Mitigating Exposure Bias in Generative XMC for User-Interest Propensity Modelling

   Abhinav Mahajan, Arindam Sarkar, and Prakash Mandayam Comar

   2026

   Under Review
3. paper

   Make-it-Pretty: Towards Generating Designs from its Components

   Abhinav Mahajan, Tripathy Abhikhya, Sudeeksha Reddy, and 3 more authors

   2026

   Under review

   Abs

   Machine learning approaches towards creating graphic designs have gained significant attention recently. They empower both experienced and novice designers to generate visually appealing and semantically rich designs with minimal effort. While creating a design, a designer would have rough idea of the components to be used (text-boxes, images, shapes etc.). A system that can take this set of components and generate designs, by transforming and composing them meaningfully, would be of great assistance to them. Towards this end, we present Components-to-Design, a framework that generates visually appealing graphic designs from its constituent components provided by users. The unique challenge in our proposed setting is to stylize and compose individual elements while maintaining overall aesthetic quality of the design. We leverage the inherent design knowledge present in Large Multimodal Models and Diffusion models towards addressing this problem. Furthermore, we develop a novel training-free approach for image composition to ensure higher input identity preservation and overall semantic coherence. By additionally allowing users to make edits and pre- serve input components exactly, our framework balances harmonization and customization. We introduce experimental protocols, adapt baselines and provide extensive quantitative and qualitative evaluation, to test the mettle of our proposed solution. We hope that our work will inspire further research along this pragmatic problem setting.

2025

1. paper

   Design-o-meter: Towards Evaluating and Refining Graphic Designs

   Sahil Goyal, Abhinav Mahajan, Swasti Mishra, and 4 more authors

   In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025

   Abs DOI

   Graphic designs are an effective medium for visual communication. They range from greeting cards to corporate flyers and beyond. Off-late, machine learning techniques are able to generate such designs, which accelerates the rate of content production. An automated way of evaluating their quality becomes critical. Towards this end, we introduce Design-o-meter, a data-driven methodology to quantify the goodness of graphic designs. Further, our approach can suggest modifications to these designs to improve its visual appeal. To the best of our knowledge, Design-o-meter is the first approach that scores and refines designs in a unified framework despite the inherent subjectivity and ambiguity of the setting. Our exhaustive quantitative and qualitative analysis of our approach against baselines adapted for the task (including recent Multimodal LLM-based approaches) brings out the efficacy of our methodology. We hope our work will usher more interest in this important and pragmatic problem setting.

2023

1. paper

   AVA: AI-driven Virtual Rehabilitation Assistant

   Ali Abedi, Tracey JF Colella, Mark Bayley, and 6 more authors

   In 15th International Conference on Virtual Rehabilitation (WCISVR), 2023

   Abs DOI

   Virtual rehabilitation has gained popularity in delivering personalized programs of exercise, education, and counseling to the home of patients. Despite the potential benefits of virtual rehabilitation programs in reducing rehospitalization and death, high dropout rates pose a significant obstacle to their effectiveness. This is due to several barriers, including a lack of motivation and confidence in completing rehabilitation exercises. This paper introduces an AI-driven Virtual Assistant (AVA) to assist patients in completing their prescribed rehabilitation exercises at home. AVA uses AI algorithms to analyze patients movements and provide them with real-time personalized feedback. The web application containing AVA can be accessed from any camera-enabled computer or mobile device without the need for additional hardware. Through a co-design approach, the movement training components of AVA for upper-limb stroke rehabilitation exercises were developed and reviewed by the research team, including a patient partner. The importance of including an avatar in virtual rehabilitation and providing realtime feedback to guide patients in performing exercises correctly was emphasized by the patient partner. AVA has the potential to enhance healthcare outreach, increase program participation and completion, and improve long-term health outcomes.