Indigenous technologies in electronics and information technology, with a focus on cybersecurity, artificial intelligence (AI), quantum technologies and the Internet of Things (IoT), were showcased at a technology roadshow held at the CSIR-National Geophysical Research Institute (NGRI) on Wednesday, 4th November 2026. MosChip Technologies CEO and managing director Kakumanu Srinivasa and IIIT Hyderabad professor Sandeep Kumar Shukla attended as guests of honour. The event, organised by the Centre for Development of Advanced Computing (C-DAC), Hyderabad, in association with SAMEER and C-MET, was a precursor to Tec-Verse 2026, scheduled for November 26 and 27 at the Chennai Trade Centre. Two parallel sessions focused on cybersecurity, quantum technologies, Al and lot.

An ordinary photograph of a room, combined with a written instruction, could help artificial intelligence create an animated human movement. This is one of five research projects from the International Institute of Information Technology, Hyderabad (IIIT‑H), selected for NeurIPS 2026, a leading international AI conference. The project, ENACT, led by Prof. Charu Sharma, uses existing AI tools to understand a photograph, plan an action and generate the corresponding movement. “Since casual images of indoor environments exist aplenty, we leveraged those along with free‑form text instruction to create animated motion,” Sharma said. The technology could help generate training material for robots, although it is currently limited to static scenes and cannot handle actions such as opening drawers or carrying objects.

The International Institute of Information Technology Hyderabad’s (IIIT-H) Robotics Research Center (RRC) and Ati Robotics have collaborated for three years to develop AI-based navigation for autonomous warehouse robots. This research focuses on non-LiDAR-based technology, using cameras, image sequences, and 3D foundational models for navigation. The approach aims to reduce the need for heavy, dense LiDAR maps, employing sparse localization based on visual landmarks. Rather than using dense LiDAR maps, IIIT-H researchers experimented with sparse localization, which estimates a robot’s position with limited data or landmarks. In indoor settings like warehouses, this involves decomposing scenes into landmarks such as “next to the door” or “opposite the elevator,” according to Professor Madhava Krishna.