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Foundation models in robotics: Applications, challenges, and the future
We survey applications of pretrained foundation models in robotics. Traditional deep
learning models in robotics are trained on small datasets tailored for specific tasks, which …
learning models in robotics are trained on small datasets tailored for specific tasks, which …
Imperative learning: A self-supervised neural-symbolic learning framework for robot autonomy
C Wang, K Ji, J Geng, Z Ren, T Fu, F Yang… - ar**
J Sun, P Mao, L Kong, J Wang - Sensors (Basel, Switzerland), 2025 - pmc.ncbi.nlm.nih.gov
Pre-trained models trained with internet-scale data have achieved significant improvements
in perception, interaction, and reasoning. Using them as the basis of embodied gras** …
in perception, interaction, and reasoning. Using them as the basis of embodied gras** …
SAT: Spatial Aptitude Training for Multimodal Language Models
Spatial perception is a fundamental component of intelligence. While many studies highlight
that large multimodal language models (MLMs) struggle to reason about space, they only …
that large multimodal language models (MLMs) struggle to reason about space, they only …
BiFold: Bimanual Cloth Folding with Language Guidance
Cloth folding is a complex task due to the inevitable self-occlusions of clothes, their
complicated dynamics, and the disparate materials, geometries, and textures that garments …
complicated dynamics, and the disparate materials, geometries, and textures that garments …
[PDF][PDF] The One RING: a Robotic Indoor Navigation Generalist
Modern robots vary significantly in shape, size, and sensor configurations used to perceive
and interact with their environments. However, most navigation policies are embodiment …
and interact with their environments. However, most navigation policies are embodiment …