SubsidieMeestersFAME: 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.
Projectdetails
Introduction
The realization of computational models for accomplishing everyday manipulation tasks for any object and any purpose would be a disruptive breakthrough in the creation of versatile, general-purpose robot agents; and it is a grand challenge for AI and robotics. Humans are able to accomplish tasks such as “cut up the fruit” for many types of fruit by generating a large variety of context-specific manipulation behaviors.
They can typically accomplish the tasks on the first attempt despite uncertain physical conditions and novel objects. Acting so effectively requires comprehensive reasoning about the possible consequences of intended behavior before physically interacting with the real world.
Research Hypothesis
In the FAME project, I will investigate the research hypothesis that a knowledge representation and reasoning (KR&R) framework based on explicitly-represented and machine-interpretable inner-world models can enable robots to contextualize underdetermined manipulation task requests on the first attempt.
To this end, I will design, implement, and evaluate FAME (Future-oriented cognitive Action Modelling Engine), a hybrid symbolic/subsymbolic KR&R framework that will contextualize actions by reasoning symbolically in an abstract and generalized manner but also by reasoning with “one’s eyes and hands” through mental simulation and imagistic reasoning.
Breakthrough Research Results
Realizing FAME requires three breakthrough research results:
- Modelling and parameterization of manipulation motion patterns and understanding the resulting effects under uncertain conditions.
- The ability to mentally simulate imagined and observed manipulation tasks to link them to the robot’s knowledge and experience.
- The on-demand acquisition of task-specific causal models for novel manipulation tasks through mental physics-based simulations.
Benchmark Challenge
To assess the power and feasibility of FAME, I will use open manipulation task learning as a benchmark challenge.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 2.499.063 |
| Totale projectbegroting | € 2.499.063 |
Tijdlijn
| Startdatum | 1-9-2023 |
| Einddatum | 31-8-2028 |
| Subsidiejaar | 2023 |
Partners & Locaties
Projectpartners
- UNIVERSITAET BREMENpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
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|---|---|---|---|---|
MANUNKIND: Determinants and Dynamics of Collaborative ExploitationThis project aims to develop a game theoretic framework to analyze the psychological and strategic dynamics of collaborative exploitation, informing policies to combat modern slavery. | ERC STG | € 1.497.749 | 2022 | Details |
Elucidating the phenotypic convergence of proliferation reduction under growth-induced pressureThe UnderPressure project aims to investigate how mechanical constraints from 3D crowding affect cell proliferation and signaling in various organisms, with potential applications in reducing cancer chemoresistance. | ERC STG | € 1.498.280 | 2022 | Details |
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MANUNKIND: Determinants and Dynamics of Collaborative Exploitation
This project aims to develop a game theoretic framework to analyze the psychological and strategic dynamics of collaborative exploitation, informing policies to combat modern slavery.
Elucidating the phenotypic convergence of proliferation reduction under growth-induced pressure
The UnderPressure project aims to investigate how mechanical constraints from 3D crowding affect cell proliferation and signaling in various organisms, with potential applications in reducing cancer chemoresistance.
The Ethics of Loneliness and Sociability
This project aims to develop a normative theory of loneliness by analyzing ethical responsibilities of individuals and societies to prevent and alleviate loneliness, establishing a new philosophical sub-field.
Uncovering the mechanisms of action of an antiviral bacterium
This project aims to uncover the mechanisms behind Wolbachia's antiviral protection in insects and develop tools for studying symbiont gene function.
Vergelijkbare projecten uit andere regelingen
| 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 STG | € 1.499.999 | 2022 | 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 STG | € 1.500.000 | 2022 | 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 COG | € 1.998.500 | 2023 | Details |
A metapredictive model of synthetic awareness for enabling tool inventionMETATOOL aims to develop a computational model of synthetic awareness in robots to enable self-evaluation and tool invention, advancing adaptive AI technology. | EIC Pathfinder | € 3.298.843 | 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.
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.
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.
A metapredictive model of synthetic awareness for enabling tool invention
METATOOL aims to develop a computational model of synthetic awareness in robots to enable self-evaluation and tool invention, advancing adaptive AI technology.