http://repository.iiitd.edu.in/xmlui/handle/123456789/1806 Year-2025 2026-05-27T21:00:58Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1969 Interactive task learning framework for human-robot collaboration using generative AI Garg, Himang Chandra; Jain, Aditya Raj; Shukla, Jainendra (Advisor); Kundu, Tanmoy (Advisor) Navigating a robot in an new environment without predefined graphs presents significant chal- lenges in perception, planning, and adaptability. However, traditional approaches rely on struc- tured maps, which limits their flex- ibility in dynamic and unexplored environments. Therefore, we propose a foundational framework that enables robots to navigate and execute tasks through human interaction in natural language. By leveraging Generative AI and multimodal learning, our system allows robots to dynamically adapt to new environments without requiring prede- fined graphs 2025-07-18T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1968 SLAM technology for VR application Chauhan, Abhijeet; Dwivedi, Ashutosh; Sankit; Shankhwar, Kalpana (Advisor) This project presents the development of a mobile telepresence system that enables real-time 3D mapping and remote environment visualization using a VR headset. At its core, the system in- tegrates an Intel RealSense D455 RGB-D camera mounted on a rover, which streams depth and color data to a ROS 2-based onboard computer. Utilizing the RTAB-Map SLAM framework, the system incrementally reconstructs a dense 3D map of the environment as the rover explores. This spatial data is visualized using RViz2 and is intended for further integration with Unity for immersive VR rendering via HTC Vive. The current implementation successfully demonstrates the capture and visualization of live camera feeds and the foundational setup for 3D mapping. This progress lays the groundwork for future enhancements such as full VR telepresence, remote control, and multi-sensor fusion. 2025-07-18T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1967 Interaction prediction between protein and ligand using 1D and 3D features Hasan, Ayaan; Dhanjal, Jaspreet Kaur (Advisor) Determining whether a drug molecule inhibits a target protein is a critical step in the drug discovery process. While the pIC50 value is commonly used to quantify the inhibitory effect of a drug, experimentally determining these values is often expensive, slow, and not feasible for large-scale screening. To address this, we propose a deep learning-based approach to classify protein-ligand interactions as inhibitory or non-inhibitory, using 1D sequence data. Our method uses protein amino acid sequences and ligand representations in the form of SMILES strings as inputs. The corresponding interaction label is derived from experimentally known pIC50 values, binarized into inhibitory and non-inhibitory classes based on a defined threshold. We utilize pretrained transformer models from the Hugging Face library to encode both protein and ligand sequences into contextual embeddings, which are then combined and passed through a neural classification head. This structure-free, sequence-only approach eliminates the need for 3D structural data, making it computationally efficient and scalable for high-throughput applications. The model is trained and evaluated on datasets involving cancer-related targets, and it demon- strates promising performance across standard binary classification metrics. Our results validate the use of transformer-based sequence models for predicting drug–target interaction classes, en- abling faster virtual screening pipelines in early-stage drug discovery. 2025-07-21T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1962 Applications of NLP in recipe texts Neelu; Vaikundam, Gurupriya; Upadhyay, Rituj; Bagler, Ganesh (Advisor) This study addresses the challenge of large-scale, multi-label recipe classification us- ing a real-world dataset of over 600,000 recipes collected from heterogeneous sources. The raw data exhibited significant noise, duplication, and label imbalance, motivating a comprehensive, multi-stage cleaning and preprocessing framework. Key steps included in- gredient normalization, instructions standardization, multi-label parsing, deduplication, and semantic category mapping into hierarchical supercategories. For modeling, we im- plemented a modular pipeline combining TF-IDF feature extraction, classical classifiers, XGBoost, and fine-tuned BERT models to capture both statistical and contextual signals. By adopting a per-supercategory strategy, we minimized cross-domain interference and achieved strong performance, with the fine-tuned BERT classifier attaining a weighted F1-score of 0.7996 and high accuracy on dominant labels. This work demonstrates how rigorous data preparation and modular modeling can enable fine-grained, interpretable recipe classification at scale, providing a robust foundation for downstream culinary ap- plications such as personalized meal planning and intelligent search. 2025-07-27T00:00:00Z