Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics
As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …
to wonder what lessons can be learned from other fields undergoing similar developments …
Siamese neural networks: An overview
D Chicco - Artificial neural networks, 2021 - Springer
Similarity has always been a key aspect in computer science and statistics. Any time two
element vectors are compared, many different similarity approaches can be used …
element vectors are compared, many different similarity approaches can be used …
Tidybot: Personalized robot assistance with large language models
For a robot to personalize physical assistance effectively, it must learn user preferences that
can be generally reapplied to future scenarios. In this work, we investigate personalization of …
can be generally reapplied to future scenarios. In this work, we investigate personalization of …
Perceiver-actor: A multi-task transformer for robotic manipulation
Transformers have revolutionized vision and natural language processing with their ability to
scale with large datasets. But in robotic manipulation, data is both limited and expensive …
scale with large datasets. But in robotic manipulation, data is both limited and expensive …
Cliport: What and where pathways for robotic manipulation
How can we imbue robots with the ability to manipulate objects precisely but also to reason
about them in terms of abstract concepts? Recent works in manipulation have shown that …
about them in terms of abstract concepts? Recent works in manipulation have shown that …
Transporter networks: Rearranging the visual world for robotic manipulation
Robotic manipulation can be formulated as inducing a sequence of spatial displacements:
where the space being moved can encompass an object, part of an object, or end effector. In …
where the space being moved can encompass an object, part of an object, or end effector. In …
Language-driven representation learning for robotics
S Karamcheti, S Nair, AS Chen, T Kollar, C Finn… - ar** is the process of picking up an object by applying forces and torques at a set of
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …
A survey on learning-based robotic gras**
Abstract Purpose of Review This review provides a comprehensive overview of machine
learning approaches for vision-based robotic gras** and manipulation. Current trends and …
learning approaches for vision-based robotic gras** and manipulation. Current trends and …