Robotics can offer numerous opportunities to a wide range of market domains in a developing country like India, such as manufacturing, agriculture, transport and logistics, space exploration, etc. However, the use of robotics in India has been mainly challenged by the high cost of adoption, lack of accessibility, and the lack of skilled talent in robotics technology. The current project, IRAS-HUB, addresses the lack of skilled talent in robotics technology in India by the establishment of three hubs in robotics and autonomous systems (RAS).Project IRAS-HUB will achieve the following results:1. 3 RAS hubs set up at and equipped with prototyping equipment and robotics software in three Indian HEIs.2. 22 faculties from Indian HEIs will be trained in RAS by reputable researchers and experts from EU HEIs.3. 8 courses in robotics at Indian HEIs will be developed and/or modernized. 4. 1 standardized training program will be developed for the continued learning of working professionals in RAS.5. 3 industry-driven pilot projects in RAS will be developed in India, one in each Indian HEI.6. 220 senior UG and PG students will be taught through the developed and modernized semester-long courses.7. 60 working professionals in RAS from other Indian HEIs and robotics industries will be formally trained in RAS through the developed training program.Project IRAS-HUB envisions to achieve the following impact:1. Development of highly knowledgeable and skilled human resources in RAS in India.2. Promotion of knowledge generation in robotics technology through basic and applied research. 3. Development of robotics technology for problem-solving in diverse sectors of India such as agriculture, transportation, etc.4. Promotion of competencies, capacity building, and training to nurture innovation and start-ups and aspiring entrepreneurs in robotics.5. Internationalization and modernization of Indian HEIs by connecting Indian HEI’s with global efforts in robotics education.

The long term vision in BAYFLEX is to create a radically new technology that uses low cost, green organic electronics for probabilistic computing in order to allow continuous and private monitoring of bio-signals on flexible substrates. The vision of flexible green AI sensors with on chip classification extends well beyond biomedical devices and the democratization of health care, with the possibility to transform sensor data at the edge of large networks. To achieve our goal, BAYFLEX will demonstrate a patch using active physiological sensors based on organic materials that interface with the soft human body and that also includes classification circuits (~ 100 transistors) fabricated using Thin Organic Large Area Electronics (TOLAE) processes. These circuits use spiking neurons realized in Organic Thin Film Transistors (OTFTs) to transform the non-stationary electrical signals from the sensors into stochastic bit streams. Bayesian inference is then used to classify the data using circuits of cascaded Muller C-elements. Taking advantage of the unique properties of organic electrochemical transistors (OECTs), low transistor count dynamic Muller C-elements are targeted. The patch will be tested on a simple task using healthy humans. The project brings together an interdisciplinary consortium with expertise in modeling emerging devices, biologically inspired circuit design, experts in machine learning involving electrophysiological data (including an SME) and teams with expertise in OTFT and OECT fabrication. BAYFLEX targets dissemination to a variety of publics including: scientists via publications in (open access) high impact journals and conferences; industrials and end-users through an industrial advisory board, a workshop and demonstrations at targeted conferences; the general public with the creation of a transferable workshop for non-scientific communities and training the next generation of experts through specialized schools and workshops.