Add to ORKGThis paper introduces a new approach for quickly adapting a multi-view visuomotor system for robots to varying camera configurations from the baseline setup. It utilises meta-learning to fine-tune the perceptual network while keeping the policy network fixed. Experimental results demonstrate a significant reduction in the number of new training episodes needed to attain baseline performance.Comment: Change of authors since further experiments based on second and third authors comments will be added to a future version of this pape
The number of advanced robot systems has been increasing in recent years yielding a large variety of...
In meta-reinforcement learning, an agent is trained in multiple different environments and attempts ...
The transformer architecture and variants presented remarkable success across many machine learning ...
Humans have a remarkable ability to learn new concepts from only a few examples and quickly adapt to...
Despite huge progress in artificial intelligence, the ability to quickly learn from few examples is ...
Deep learning holds promise for learning complex patterns from data, which is especially useful when...
This paper reports on a continuing research effort to evolve robot controllers with neural networks ...
In machine learning, meta-learning methods aim for fast adaptability to unknown tasks using prior kn...
International audienceDynamic uncontrolled human-robot interaction requires robots to be able to ada...
Engineered or hard-coded autonomous behaviors tend to be “brittle, ” working for a narrow range of c...
Learned visuomotor policies have shown considerable success as an alternative to traditional, hand-c...
Traditional approaches to the use of machine learning algorithms do not provide a method to learn mu...
Much like humans, robots should have the ability to leverage knowledge from previously learned tasks...
The successful operation of mobile robots requires them to adapt rapidly to environmental changes. T...
Imitation learning has gained immense popularity because of its high sample-efficiency. However, in ...
The number of advanced robot systems has been increasing in recent years yielding a large variety of...
In meta-reinforcement learning, an agent is trained in multiple different environments and attempts ...
The transformer architecture and variants presented remarkable success across many machine learning ...
Humans have a remarkable ability to learn new concepts from only a few examples and quickly adapt to...
Despite huge progress in artificial intelligence, the ability to quickly learn from few examples is ...
Deep learning holds promise for learning complex patterns from data, which is especially useful when...
This paper reports on a continuing research effort to evolve robot controllers with neural networks ...
In machine learning, meta-learning methods aim for fast adaptability to unknown tasks using prior kn...
International audienceDynamic uncontrolled human-robot interaction requires robots to be able to ada...
Engineered or hard-coded autonomous behaviors tend to be “brittle, ” working for a narrow range of c...
Learned visuomotor policies have shown considerable success as an alternative to traditional, hand-c...
Traditional approaches to the use of machine learning algorithms do not provide a method to learn mu...
Much like humans, robots should have the ability to leverage knowledge from previously learned tasks...
The successful operation of mobile robots requires them to adapt rapidly to environmental changes. T...
Imitation learning has gained immense popularity because of its high sample-efficiency. However, in ...
The number of advanced robot systems has been increasing in recent years yielding a large variety of...
In meta-reinforcement learning, an agent is trained in multiple different environments and attempts ...
The transformer architecture and variants presented remarkable success across many machine learning ...