AutoMAP (2015-2017) (EU FP7 EUROC AutoMAP) is the short name for "Autonomous Mobile Manipulation" addressing applications of robotic mobile manipulation in unstructured environments as found at CERN. This project is based on use case operations to be carried out on CERN’s flagship accelerator, the Large Hadron Collider. The main objective is to carry out the maintenance work using a remotely controlled robot mobile manipulator to reduce maintenance personnel exposure to hazards in the LHC tunnels – such as ionising radiation and oxygen deficiency hazards. A second goal is to allow the robot being able to autonomously carry out the same tasks in the assembly facility as in the tunnel on collimators during their initial build and quality assurance through the robot learning technologies. >> For more details
VINUM (2018-2023) (Italian Project) is the short name for "Grape Vine Recognition, Manipulation and Winter Pruning Automation". The objective is to apply the state-of-the-art mobile manipulation platforms and systems, a wheeled mobile platform with a commercial full torque sensing arm and multiple sensors, and an under-develop quadruped robot mobile platform with a customized robotic arm and multiple sensors (collaboration with Dr. Claudio Semini) for various maintenance and automation work in the vineyard, e.g., pruning, inspecting to tackle the shortage of skilled workers. Together with project, VINUM is targeting at providing very robust solutions for outdoor application to deal with all kinds of natural objects. There are two activities carried out under the project using our developed mobile manipulators: (i) Grape Vine Winter Pruning; (ii) Table Grape Harvesting.
LEARN-REAL (2019-2022) (EU H2020 Chist-Era Learn-Real) is the short name for "Learning Physical Manipulation Skills with Simulators Using Realistic Variations". It proposes an innovative toolset comprising: i) a simulator with realistic rendering of variations allowing the creation of datasets and the evaluation of algorithms in new situations; ii) a virtual-reality interface to interact with the robots within their virtual environments, to teach robots object manipulation skills in multiple configurations of the environment; and iii) a web-based infrastructure for principled, reproducible and transparent benchmarking of learning algorithms for object recognition and manipulation by robots. Strong AI oriented technologies (Deep Reinforcement Learning, Deep Learning, Sim2Real transfer learning) are our main concerns in this project. >> For more details
LEARN-ASSIST (2021-2023) (Italy-Japan Government Cooperation in Science and Technology) is the short name for ''Assistive Robotic System for Various Dressing Tasks through Robot Learning by Demonstration via Sim-to-Real Methods". The objective is to develop an AI empowered general purpose robotic system for dexterous manipulation of complex and unknown objects in rapidly changing, dynamic and unpredictable real-world environments. This will be achieved through intuitive embodied robotic demonstration between the human operator enhanced with a motion tracking device and the robot controller empowered with AI-based vision and learning skills. The privileged use case of such a system is assistance for patients or elders with limited physical ability in their daily life object manipulation tasks, e.g., dressing of various clothes. (project website coming soon)