AssemblySkills
Financiado
Acquiring assembly skills by robot learning
Present-day industrial robots are made for the purpose of repeating several tasks thousands of times. What the
manufacturing industry needs instead is a robot that can do thousands of tasks, a few times. Programming a robot to sol...
Present-day industrial robots are made for the purpose of repeating several tasks thousands of times. What the
manufacturing industry needs instead is a robot that can do thousands of tasks, a few times. Programming a robot to solve
just one complex motor task has remained a challenging, costly and time-consuming task. In fact, it has become the key
bottleneck in industrial robotics. Empowering robots with the ability to autonomously learn such tasks is a promising
approach, and, in theory, machine learning has promised fully adaptive control algorithms which learn both by observation
and trial-and-error. However, to date, learning techniques have yet to fulfil this promise, as only few methods manage to
scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of collaborative robots.
The goal of the AssemblySkills ERC PoC is to validate an autonomous skill learning system that enables industrial robots to
acquire and improve a rich set of motor skills. Using structured, modular control architectures is a promising concept to scale
robot learning to more complex real-world tasks. In such a modular control architecture, elemental building blocks – called
movement primitives, need to be adapted, sequenced or co-activated simultaneously. Within the ERC PoC AssemblySkills,
our goal is to group these modules into an industry-scale complete software package that can enable industrial robots to
learn new skills (particularly in assembly tasks). The value proposition of our ERC PoC is a cost-effective, novel machine
learning system that can unlock the potential of manufacturing robots by enabling them to learn to select, adapt and
sequence parametrized building blocks such as movement primitives. Our approach is unique in the sense that it can
acquire more than just a single desired trajectory as done in competing approaches, capable of save policy adaptation,
requires only little data and can explain the solution to the robot operator.
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Duración del proyecto: 19 meses
Fecha Inicio: 2020-11-18
Fecha Fin: 2022-06-30
Fecha Fin: 2022-06-30
Línea de financiación: concedida
El organismo H2020 notifico la concesión del proyecto el día 2022-06-30
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2020-POC:
Call for proposals for ERC Proof of Concept Grant
Cerrada
hace 4 años
Presupuesto
El presupuesto total del proyecto asciende a
150K€
Líder del proyecto
TECHNISCHE UNIVERSITAT DARMSTADT
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5
1.05M€ |
Participante