22,889 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
Training neural networks to encode symbols enables combinatorial generalization
Combinatorial generalization - the ability to understand and produce novel
combinations of already familiar elements - is considered to be a core capacity
of the human mind and a major challenge to neural network models. A significant
body of research suggests that conventional neural networks can't solve this
problem unless they are endowed with mechanisms specifically engineered for the
purpose of representing symbols. In this paper we introduce a novel way of
representing symbolic structures in connectionist terms - the vectors approach
to representing symbols (VARS), which allows training standard neural
architectures to encode symbolic knowledge explicitly at their output layers.
In two simulations, we show that neural networks not only can learn to produce
VARS representations, but in doing so they achieve combinatorial generalization
in their symbolic and non-symbolic output. This adds to other recent work that
has shown improved combinatorial generalization under specific training
conditions, and raises the question of whether specific mechanisms or training
routines are needed to support symbolic processing
FiLM: Visual Reasoning with a General Conditioning Layer
We introduce a general-purpose conditioning method for neural networks called
FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network
computation via a simple, feature-wise affine transformation based on
conditioning information. We show that FiLM layers are highly effective for
visual reasoning - answering image-related questions which require a
multi-step, high-level process - a task which has proven difficult for standard
deep learning methods that do not explicitly model reasoning. Specifically, we
show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error
for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are
robust to ablations and architectural modifications, and 4) generalize well to
challenging, new data from few examples or even zero-shot.Comment: AAAI 2018. Code available at http://github.com/ethanjperez/film .
Extends arXiv:1707.0301
Metric Learning for Generalizing Spatial Relations to New Objects
Human-centered environments are rich with a wide variety of spatial relations
between everyday objects. For autonomous robots to operate effectively in such
environments, they should be able to reason about these relations and
generalize them to objects with different shapes and sizes. For example, having
learned to place a toy inside a basket, a robot should be able to generalize
this concept using a spoon and a cup. This requires a robot to have the
flexibility to learn arbitrary relations in a lifelong manner, making it
challenging for an expert to pre-program it with sufficient knowledge to do so
beforehand. In this paper, we address the problem of learning spatial relations
by introducing a novel method from the perspective of distance metric learning.
Our approach enables a robot to reason about the similarity between pairwise
spatial relations, thereby enabling it to use its previous knowledge when
presented with a new relation to imitate. We show how this makes it possible to
learn arbitrary spatial relations from non-expert users using a small number of
examples and in an interactive manner. Our extensive evaluation with real-world
data demonstrates the effectiveness of our method in reasoning about a
continuous spectrum of spatial relations and generalizing them to new objects.Comment: Accepted at the 2017 IEEE/RSJ International Conference on Intelligent
Robots and Systems. The new Freiburg Spatial Relations Dataset and a demo
video of our approach running on the PR-2 robot are available at our project
website: http://spatialrelations.cs.uni-freiburg.d
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns
visual concepts, words, and semantic parsing of sentences without explicit
supervision on any of them; instead, our model learns by simply looking at
images and reading paired questions and answers. Our model builds an
object-based scene representation and translates sentences into executable,
symbolic programs. To bridge the learning of two modules, we use a
neuro-symbolic reasoning module that executes these programs on the latent
scene representation. Analogical to human concept learning, the perception
module learns visual concepts based on the language description of the object
being referred to. Meanwhile, the learned visual concepts facilitate learning
new words and parsing new sentences. We use curriculum learning to guide the
searching over the large compositional space of images and language. Extensive
experiments demonstrate the accuracy and efficiency of our model on learning
visual concepts, word representations, and semantic parsing of sentences.
Further, our method allows easy generalization to new object attributes,
compositions, language concepts, scenes and questions, and even new program
domains. It also empowers applications including visual question answering and
bidirectional image-text retrieval.Comment: ICLR 2019 (Oral). Project page: http://nscl.csail.mit.edu
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