https://peertube.laas.fr/videos/watch/93459040-989c-4d98-8b64-25af82491b66 Abstract. Environmental monitoring is becoming increasingly important to address biodiversity loss, climate change, and ecosystem degradation. Traditional monitoring approaches are often labor-intensive, expensive, and limited in spatial and temporal coverage. Recent advances in robotics and artificial intelligence offer new opportunities to develop autonomous systems capable of collecting high-quality environmental data in complex and unstructured natural environments. This talk presents the Natural Intelligence approach, developed within the European H2020 Natural Intelligence (NI) project, which draws inspiration from the adaptive capabilities of biological systems to design robots that are resilient, efficient, and able to operate with minimal human intervention. Rather than relying solely on increasingly complex AI models, the NI paradigm combines morphology, embodied intelligence, and learning to create robotic systems that naturally adapt to challenging environments. The presentation will illustrate how legged robots equipped with multimodal sensing can autonomously navigate forests, wetlands, and protected natural areas, acquiring environmental data while minimizing disturbance to ecosystems. Examples from real-world field deployments will demonstrate how adaptive locomotion, autonomous mission planning, and AI-based perception enable long-term biodiversity monitoring, habitat assessment, and ecological data collection. The talk concludes by discussing how Natural Intelligence can contribute to the next generation of environmental robotics, supporting scientists, conservation agencies, and public authorities in building scalable, sustainable, and data-driven ecosystem monitoring solutions. www.nih2020.eu Bio. Manolo Garabini graduated in Mechanical Engineering and received a Ph.D. degree in Robotics from the University of Pisa where he is currently employed as an Associate Professor. His main research interests are in the design, planning, and control of soft and adaptive robots, from actuators to end-effectors (hands, grippers, feet), to complex multi-dof systems. A part of his activity has been devoted to theoretically proving the effectiveness of soft and adaptive robots in high performance, high efficiency, and resilient tasks via analytical and numerical optimization tools. He contributed to the realization of modular Variable Stiffness Actuators. He contributed to the design of the joints and the lower body of the humanoid robot WALK-MAN and participated in the DARPA Robotics Challenge and at a field test in Amatrice, Italy after a disastrous earthquake event. Moreover, he co-designed hardware and software of a dual-arm robot - WRAPP-up - for flexible picking and palletizing of goods, and of a robot for film unwrapping. Recently he contributed to the development of several algorithms, based on optimal control and learning, for motion planning and control in minimum time and under uncertainty. He has been the Principal Investigator in the THING H2020 EU Research Project for the University of Pisa, the coordinator of the NI H2020 project, and of the Dysturbance sub-project of the EUROBENCH H2020 project. He is now the PI of the personal grant FISA-2023 OCCAM and he is the co-founder of two companies, qbrobotics s.r.l., and XStar Motion s.r.l. Tue, 21 Jul 2026 05:31:58 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://peertube.laas.fr https://peertube.laas.fr/lazy-static/avatars/d5a80afa-76a2-4f1e-84ca-ce63a74b79ab.png https://peertube.laas.fr/videos/watch/93459040-989c-4d98-8b64-25af82491b66 All rights reserved, unless otherwise specified in the terms specified at https://peertube.laas.fr/about and potential licenses granted by each content's rightholder.