Learning affordances in object-centric generative models
Given visual observations of a reaching task together with a stick-like tool, we propose a novel approach that learns to exploit task-relevant object affordances by combining generative modelling with a task-based performance predictor. The embedding learned by the generative model captures the fact...
Váldodahkkit: | Wu, Y, Kasewa, S, Groth, O, Salter, S, Sun, L, Parker Jones, O, Posner, H |
---|---|
Materiálatiipa: | Conference item |
Giella: | English |
Almmustuhtton: |
International Conference on Machine Learning
2020
|
Geahča maid
-
Reconstruction bottlenecks in object-centric generative models
Dahkki: Engelcke, M, et al.
Almmustuhtton: (2020) -
GENESIS: generative scene inference and sampling of object-centric latent representations
Dahkki: Engelcke, M, et al.
Almmustuhtton: (2020) -
APEX: Unsupervised, object-centric scene segmentation and tracking for robot manipulation
Dahkki: Wu, Y, et al.
Almmustuhtton: (2021) -
Object-centric generative models for robot perception and action
Dahkki: Wu, Y
Almmustuhtton: (2023) -
DreamUp3D: object-centric generative models for single-view 3D scene understanding and real-to-sim transfer
Dahkki: Wu, Y, et al.
Almmustuhtton: (2024)