Added MAGICIAN and INVERSE page content.

content/authors/matteodallevedove/_index.md

@@ -8,7 +8,7 @@ last_name: Dalle Vedove
 
 # Username (this should match the folder name)
 authors:
-  - matteodv
+  - matteodallevedove
 
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 superuser: false

content/project/inverse/index.md

@@ -1,6 +1,22 @@
 ---
-title: Inverse
-summary: Interactive robots that intuitively learn to invert tasks by reasoning about their execution, Horizon Europe EU project.
+title: INVERSE
+subtitle: INteractive robots that intuitiVely lEarn to inVErt tasks by ReaSoning about their Execution.
+summary: Advancing robotics technology through continous learning and adaptation.
+tags: ["Robot Learning", "Manufacturing"]
+authors: [matteosaveriano]
 ---
 
-Work in progress.
+This is a research and innovation project funded by Horizon Europe. Website: [https://www.inverse-project.org/](https://www.inverse-project.org/).
+
+
+## Vision
+
+The scientific vision of INVERSE is to endow robots with the cognitive capabilities needed to synthesise, monitor, and execute inverse plans from direct tasks defined in terms of human-understandable instructions and procedures.
+
+## Challenges
+
+Recent advancements in Artificial Intelligence (AI) have improved robot autonomy and manipulation tasks but fall short in enabling sophisticated interactions with humans and adapting to new environments. While robots can now operate closer to humans, they lack the necessary cognitive capabilities to understand and execute tasks in varied domains, similar to human adaptability and problem-solving.
+
+## Solution
+
+The INVERSE project aims to provide robots with these essential cognitive abilities by adopting a continual learning approach. After an initial bootstrap phase, used to create initial knowledge from human-level specifications, the robot refines its repertoire by capitalising on its own experience and on human feedback. This experience-driven strategy permits to frame different problems, like performing a task in a different domain, as a problem of fault detection and recovery. Humans have a central role in INVERSE, since their supervision helps limit the complexity of the refinement loop, making the solution suitable for deployment in production scenarios. The effectiveness of developed solutions will be demonstrated in two complementary use cases designed to be a realistic instantiation of the actual work environments.

content/project/magician/index.md

@@ -1,6 +1,51 @@
 ---
-title: Magician
-summary: Immersive learninG for imperfection detection and repair through human-robot interaction, Horizon Europe EU project.
+title: MAGICIAN
+subtitle: Immersive learninG for imperfection detection and repair through human-robot interaction.
+summary:
+tags: ["Smart Industrial Robotics", "Automotive"]
+authors: [danielefontanelli, luigipalopoli, matteodallevedove, songqungao]
 ---
 
-Work in progress.
+This is a research and innovation project funded by Horizon Europe. Website: [https://magician-project.eu/](https://magician-project.eu/).
+
+- 4-year EU project: October 2023-September 2027
+- 11 cosortium partners from 7 countries coordinated by Università di Trento
+- 1 Automotive Use Case and extension of Use Cases through 2 Open Calls
+
+## Vision
+
+MAGICIAN will develop robotic solutions to classify and rework defects from semi-finished products autonomously before the finalization of product aesthetics.
+
+These solutions are designed to be **modular**, applicable to **various manufacturing fields**, **reducing physical strain** and **enhancing safety** for human operators.
+
+
+
+## Challenges
+
+Consumers increasingly expect manufacturing products to be free of defects which sets high standards for the production process. However, associated working processes are physically and cognitively demanding for workers and executed in a potentially hazardous environment.
+
+
+
+## Solutions
+
+Two modular robotic solutions:
+- a sensing robot for defect analysis (SR)
+- a cleaning robot for reworking defects (CR) 
+
+Both robots will use AI modules to perform associated operations.
+
+Data needed for these AI modules will be gathered by learning from workers operating on semi-finished products.
+
+**Human-centred approach**: MAGICIAN applies a  human-centred design strategy to shape the progress of automation and human-robot collaboration in manufacturing towards an emphasis on trust, empathy, and ethics.
+
+**Use Cases**: MAGICIAN solutions will be tested in an automotive manufacturing use case. Both robots will be coupled with human operators during the testing to ensure trust-based human-robot collaboration.  Additional use cases will be engaged through two Open Calls.
+
+
+
+## Impact
+
+Innovative robotic components for mechanical working operations allowing for human-robot collaboration
+Improved productivity in manufacturing and maintenance
+Improved health and safety conditions for human workers and focus on added value operations
+Tested applicability of solutions for various manufacturing application fields
+Strengthened trust in AI and robotic technologies