The Artificial Motion Factory
ARTIFACT aims to revolutionize robot autonomy by developing a modular AI control architecture that enables advanced decision-making and interaction in dynamic environments through learning and perception.
Projectdetails
Introduction
Today’s robots are confined to tightly controlled environments: even the complex choreographies that the Atlas humanoid flawlessly executes heavily rely on handcrafted control strategies and detailed workspace models, with little place for sensing. To put it bluntly, robots are nowhere near the level of agility, dexterity, and even less so autonomy, robustness, and safety required for their deployment “in the wild” alongside people.
Project Overview
The tenet of ARTIFACT is that the key to an actual revolution will come from the algorithmic foundations of artificial motion intelligence, an AI challenged from the start to interact physically with dynamic environments and, ultimately, people.
Approach
To achieve this, we will break away from the dichotomy between:
- Optimal Control: Where the role of perception is traditionally limited to an early state estimation stage.
- Reinforcement Learning: Where control policies are typically learned model-free with no guarantee to cope with the curse of dimensionality.
Control Architecture
In ARTIFACT, we will devise a unified, structured, modular, and learnable control architecture for providing robots with advanced decision-making capabilities to solve complex tasks and face new interactions as they experience the world.
Key Features
This architecture will leverage the notion of differentiable programming at all scales to enable robots to:
- Capture models of their interactions directly from a sound combination of sensor data and first principles from physics.
- Autonomously discover new complex gestures and movements leveraging their past experiences.
- Learn embodied representations to control their interactions finely and reason about the physical world.
Implementation
It will be implemented in open-source software and shown in real-world and challenging scenarios requiring fine dexterity and high agility.
Conclusion
Altogether, these contributions will be the key enablers to enhance robot autonomy fundamentally, thus opening the age of ubiquitous robots at the service of mankind.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 1.499.955 |
Totale projectbegroting | € 1.499.955 |
Tijdlijn
Startdatum | 1-9-2025 |
Einddatum | 31-8-2030 |
Subsidiejaar | 2025 |
Partners & Locaties
Projectpartners
- INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUEpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
Project | Regeling | Bedrag | Jaar | Actie |
---|---|---|---|---|
Intuitive interaction for robots among humansThe INTERACT project aims to enable mobile robots to safely and intuitively interact with humans in complex environments through innovative motion planning and machine learning techniques. | ERC Starting... | € 1.499.999 | 2022 | Details |
Autonomous Robots with Common SenseThis project aims to develop an 'Artificial Physical Awareness' autopilot system for autonomous robots, enabling them to operate safely and effectively despite failures by understanding their limitations. | ERC Consolid... | € 1.996.040 | 2024 | Details |
Model-based Reinforcement Learning for Versatile Robots in the Real WorldREAL-RL aims to create versatile autonomous robots that learn from experience using a model-based approach for efficient task adaptation and behavior planning. | ERC Consolid... | € 1.998.500 | 2023 | Details |
FAME: OPEN-ENDED MANIPULATION TASK LEARNING WITH FAME (FUTURE-ORIENTED COGNITIVE1 ACTION MODELLING ENGINE)The FAME project aims to develop a hybrid KR&R framework enabling robots to perform diverse manipulation tasks effectively on the first attempt through contextual reasoning and mental simulation. | ERC Advanced... | € 2.499.063 | 2023 | Details |
Deep Bayesian Reinforcement Learning -- Unifying Perception, Planning, and ControlDevelop an algorithmic framework using deep learning and Bayesian reinforcement learning to enhance robotic manipulation in unstructured environments by effectively managing uncertainty. | ERC Starting... | € 1.500.000 | 2022 | Details |
Intuitive interaction for robots among humans
The INTERACT project aims to enable mobile robots to safely and intuitively interact with humans in complex environments through innovative motion planning and machine learning techniques.
Autonomous Robots with Common Sense
This project aims to develop an 'Artificial Physical Awareness' autopilot system for autonomous robots, enabling them to operate safely and effectively despite failures by understanding their limitations.
Model-based Reinforcement Learning for Versatile Robots in the Real World
REAL-RL aims to create versatile autonomous robots that learn from experience using a model-based approach for efficient task adaptation and behavior planning.
FAME: OPEN-ENDED MANIPULATION TASK LEARNING WITH FAME (FUTURE-ORIENTED COGNITIVE1 ACTION MODELLING ENGINE)
The FAME project aims to develop a hybrid KR&R framework enabling robots to perform diverse manipulation tasks effectively on the first attempt through contextual reasoning and mental simulation.
Deep Bayesian Reinforcement Learning -- Unifying Perception, Planning, and Control
Develop an algorithmic framework using deep learning and Bayesian reinforcement learning to enhance robotic manipulation in unstructured environments by effectively managing uncertainty.
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METATOOL aims to develop a computational model of synthetic awareness in robots to enable self-evaluation and tool invention, advancing adaptive AI technology.
Reaction robot with intimate photocatalytic and separation functions in a 3-D network driven by artificial intelligence
CATART aims to develop autonomous reaction robots using AI and 3-D quantum dot networks to efficiently mimic natural chemical production, enhancing productivity and sustainability in the chemical industry.
Perception of Collaborative Robots
Het project onderzoekt de haalbaarheid van technieken zoals voice control en machine vision om collaboratieve robots beter omgevingsbewust te maken voor gebruik in high-mix low-volume productie.
Robot Control-as-a-Platform
Het project onderzoekt de haalbaarheid van een softwareoplossing voor generieke aansturing van flexfeeders in productieprocessen.
Mens Robot Interactie op Afstand
Het project onderzoekt de haalbaarheid van een innovatieve mens-robot interactieoplossing om de samenwerking tussen mens en robot in ongestructureerde omgevingen te verbeteren en kosten te verlagen.