Cosimo Della Santina is Assistant Professor at TU Delft and Research Scientist at the German Aerospace Institute (DLR) since 2020. He received his PhD in robotics (cum laude, 2019) from University of Pisa. He was then a visiting PhD student and a postdoc (2017 to 2019) at the Massachusetts Institute of Technology (MIT), and a postdoc (2020) and guest lecturer (2021) at the Technical University of Munich (TUM). His work has been recognized through awards including the euRobotics Georges Giralt Ph.D. Award (2020), the “Fabrizio Flacco” Young Author Award of the RAS Italian chapter (2019), and the European Embedded Control Institute PhD award (finalist, 2020). Cosimo is currently a member of several EBs (ICRA, IROS, RAL, Frontiers). His main research interests include (i) Modelling for Control and Model Based Control of Soft Robots, (ii) Combining Machine Learning and Model Based Strategies, (iii) Soft Robotic Hands/prostheses.

cosimodellasantina.eu

Talk #1: Combining physical and artificial intelligence in hand-centric grasping and manipulation

The classic approach to grasping and manipulation with rigid robotic hands generally favored object-centric analytical solutions, which – although very elegant and theoretically sound – has not yet produced the desired outcomes in the practice. This talk aims at discussing a different path, which entails moving the focus from the object to the robotic hand. This shift of perspective opens up several challenges which require a full integration of models, materials, machine learning, and bio-inspiration to be tackled effectively. Combining these ingredients, control intelligence can be embedded directly in the hand mechanics. As a result, soft end-effectors can achieve high-level grasping and manipulation performance when operated by humans. However, such a level of dexterity is still unmatched in autonomous grasp execution. Indeed, classic approaches cannot be applied to this kind of hands, which – by their own nature – do not allow fingertips placement with the required precision and relative independence. On the contrary, data driven approaches could be the key to learn from humans how to manage soft hands, towards higher levels of autonomous grasping capabilities.

Primary Keywords: Modeling, Control, and Learning for Soft Robots; Motion Control; Multifingered Hands; Legged Robots

Secondary Keywords:  Model Learning for Control; Dexterous Manipulation; Passive Walking; Soft Robot Applications; Natural Machine Motion; Deep Learning in Robotics and Automation

T-RO AE: July 1, 2023 – June 30, 2026