44,529 research outputs found
Object-Oriented Dynamics Learning through Multi-Level Abstraction
Object-based approaches for learning action-conditioned dynamics has
demonstrated promise for generalization and interpretability. However, existing
approaches suffer from structural limitations and optimization difficulties for
common environments with multiple dynamic objects. In this paper, we present a
novel self-supervised learning framework, called Multi-level Abstraction
Object-oriented Predictor (MAOP), which employs a three-level learning
architecture that enables efficient object-based dynamics learning from raw
visual observations. We also design a spatial-temporal relational reasoning
mechanism for MAOP to support instance-level dynamics learning and handle
partial observability. Our results show that MAOP significantly outperforms
previous methods in terms of sample efficiency and generalization over novel
environments for learning environment models. We also demonstrate that learned
dynamics models enable efficient planning in unseen environments, comparable to
true environment models. In addition, MAOP learns semantically and visually
interpretable disentangled representations.Comment: Accepted to the Thirthy-Fourth AAAI Conference On Artificial
Intelligence (AAAI), 202
Grounding semantics in robots for Visual Question Answering
In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning
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