Summary
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.
In the FAME project, I will investigate the research hypothesis that a knowledge representation and reasoning (KR&R) framework
based on explictly-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. Realizing FAME requires three breakthrough research results:
(1) modelling and parameterization of manipulation motion patterns and understanding the resulting effects under
uncertain conditions;
(2) the ability to mentally simulate imagined and observed manipulation tasks to link them to the robot’s knowledge and experience;
and
(3) the on-demand acquisition of task-specific causal models for novel manipulation tasks through mental physics-based simulations.
To assess the power and feasibility of FAME, I will use open manipulation task learning as a benchmark challenge.
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.
In the FAME project, I will investigate the research hypothesis that a knowledge representation and reasoning (KR&R) framework
based on explictly-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. Realizing FAME requires three breakthrough research results:
(1) modelling and parameterization of manipulation motion patterns and understanding the resulting effects under
uncertain conditions;
(2) the ability to mentally simulate imagined and observed manipulation tasks to link them to the robot’s knowledge and experience;
and
(3) the on-demand acquisition of task-specific causal models for novel manipulation tasks through mental physics-based simulations.
To assess the power and feasibility of FAME, I will use open manipulation task learning as a benchmark challenge.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101098006 |
| Start date: | 01-09-2023 |
| End date: | 31-08-2028 |
| Total budget - Public funding: | 2 499 063,00 Euro - 2 499 063,00 Euro |
Cordis data
Original description
The realization of computational models for accomplishing everyday manipulation tasks for any object and any purpose would be adisruptive 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.
In the FAME project, I will investigate the research hypothesis that a knowledge representation and reasoning (KR&R) framework
based on explictly-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. Realizing FAME requires three breakthrough research results:
(1) modelling and parameterization of manipulation motion patterns and understanding the resulting effects under
uncertain conditions;
(2) the ability to mentally simulate imagined and observed manipulation tasks to link them to the robot’s knowledge and experience;
and
(3) the on-demand acquisition of task-specific causal models for novel manipulation tasks through mental physics-based simulations.
To assess the power and feasibility of FAME, I will use open manipulation task learning as a benchmark challenge.
Status
SIGNEDCall topic
ERC-2022-ADGUpdate Date
31-07-2023
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