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Adeli, H., Ahn, S., & Zelinsky, G. (2021). Recurrent Visual scenes are often comprised of sets of independent Experiment demonstrations of the proposed latent methods based on Video for the paper "Reasoning About Physical Interactions ICRA 2018 Spotlight Video Interactive Session Thu PM Pod J.1 Authors: Devin, Coline; Abbeel, Pieter; Darrell, Trevor; Levine, ... IEEE ICRA 2022 paper Authors: Yoann Fleytoux and Anji Ma and Serena Ivaldi and Jean-Baptiste Mouret Inria, CNRS, Université ...

En este video vamos a analizar de manera técnica un paper de Google sobre Slot

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Unlocking Object Centric Learning with Slot Attention
SORNet: Spatial Object-Centric Representations for Sequential Manipulation
Learning Dynamic Object-centric Representations for Robotic Manipulation
Reasoning About Physical Interactions with Object-Centric Models, ICLR 2019
Object-Centric Learning with Slot Attention
Deep Object-Centric Representations for Generalizable Robot Learning
Data-efficient learning of object-centric grasp preferences
SORT3D: Spatial Object-centric Reasoning Toolbox for Zero-Shot 3D Grounding Using LLMs
Shepherding Slots to Objects: Towards Stable and Robust Object-Centric Learning
GOOGLE | Object-Centric Learning with Slot Attention | Paper Explicado
Learning from Physical Human Feedback: An Object-Centric One-Shot Adaptation Method
CoRL 2020, Spotlight Talk 176: Learning to Compose Hierarchical Object-Centric Controllers for Ro...

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Last Updated: June 23, 2026

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Media Gallery

Learning object centric attention using a recurrent dorsal ventral neural Network
Learning object centric attention using a recurrent dorsal ventral neural Network neuromatch 4 nmc4
Object-Centric Learning with Slot Attention (Paper Explained)
Unlocking Object Centric Learning with Slot Attention
SORNet: Spatial Object-Centric Representations for Sequential Manipulation
Learning Dynamic Object-centric Representations for Robotic Manipulation
Reasoning About Physical Interactions with Object-Centric Models, ICLR 2019
Object-Centric Learning with Slot Attention