6 research outputs found
A Deep Incremental Boltzmann Machine for Modeling Context in Robots
Context is an essential capability for robots that are to be as adaptive as
possible in challenging environments. Although there are many context modeling
efforts, they assume a fixed structure and number of contexts. In this paper,
we propose an incremental deep model that extends Restricted Boltzmann
Machines. Our model gets one scene at a time, and gradually extends the
contextual model when necessary, either by adding a new context or a new
context layer to form a hierarchy. We show on a scene classification benchmark
that our method converges to a good estimate of the contexts of the scenes, and
performs better or on-par on several tasks compared to other incremental models
or non-incremental models.Comment: 6 pages, 5 figures, International Conference on Robotics and
Automation (ICRA 2018
Learning to Generate Unambiguous Spatial Referring Expressions for Real-World Environments
Referring to objects in a natural and unambiguous manner is crucial for
effective human-robot interaction. Previous research on learning-based
referring expressions has focused primarily on comprehension tasks, while
generating referring expressions is still mostly limited to rule-based methods.
In this work, we propose a two-stage approach that relies on deep learning for
estimating spatial relations to describe an object naturally and unambiguously
with a referring expression. We compare our method to the state of the art
algorithm in ambiguous environments (e.g., environments that include very
similar objects with similar relationships). We show that our method generates
referring expressions that people find to be more accurate (30% better)
and would prefer to use (32% more often).Comment: International Conference on Intelligent Robots and Systems (IROS
2019), Demo 1: Finding the described object (https://youtu.be/BE6-F6chW0w),
Demo 2: Referring to the pointed object (https://youtu.be/nmmv6JUpy8M),
Supplementary Video (https://youtu.be/sFjBa_MHS98
CINet: A Learning Based Approach to Incremental Context Modeling in Robots
There have been several attempts at modeling context in robots. However,
either these attempts assume a fixed number of contexts or use a rule-based
approach to determine when to increment the number of contexts. In this paper,
we pose the task of when to increment as a learning problem, which we solve
using a Recurrent Neural Network. We show that the network successfully (with
98\% testing accuracy) learns to predict when to increment, and demonstrate, in
a scene modeling problem (where the correct number of contexts is not known),
that the robot increments the number of contexts in an expected manner (i.e.,
the entropy of the system is reduced). We also present how the incremental
model can be used for various scene reasoning tasks.Comment: The first two authors have contributed equally, 6 pages, 8 figures,
International Conference on Intelligent Robots (IROS 2018
Robotlarda hiyerarşik arttırımlı bağlam modellenmesi.
Context is very crucial for robots to be able to adapt themselves to circumstances and to fulfill their tasks accordingly. There have been many studies on modeling context on robots, however, these studies either do not construct an incremental and hierarchical structure (i.e., use a fixed number of contexts and context layers) or determine the necessity of adding a new context by using rule-based approaches. In this thesis, we propose two different methods to model context. In the first method, we extend the Restricted Boltzmann Machines, a generative associative model, by incrementing the number of contexts and context layers when needed. This model constructs the hierarchical and incremental contextual representations by considering the confidence of the objects and contexts after each new scene encountered. Moreover, this deep incremental model obtains better or on-par results when compared to the incremental or non-incremental models in the literature on different tasks. In the second method, in contrast to our first method and the methods in the literature, determining the necessity of adding a new context is formulated as a learning problem. In order to be able to do that, Latent Dirichlet Allocation (LDA) model is used to generate the data with known number of contexts. The intermediate LDA models with/without the correct number of contexts are then fed to a Recurrent Model, which is trained to predict whether to add a new context or not. Our analysis on artificial and real datasets demonstrate that such a learning-based approach generalizes well, and is a promising approach for solving such incremental problems.M.S. - Master of Scienc
Learning to Increment A Contextual Model
In this paper, we summarized our efforts on incremental construction of latent variables in context (topic) models. With our models, an agent can incrementally learn a representation of critical contextual information. We demonstrated that a learning-based formulation outperforms rule-based models, and generalizes well across many settings and to real dat
CINet: A Learning Based Approach to Incremental Context Modeling in Robots
There have been several attempts at modeling context in robots. However, either these attempts assume a fixed number of contexts or use a rule-based approach to determine when to increment the number of contexts. In this paper, we pose the task of when to increment as a learning problem, which we solve using a Recurrent Neural Network. We show that the network successfully (with 98% testing accuracy) learns to predict when to increment, and demonstrate, in a scene modeling problem (where the correct number of contexts is not known), that the robot increments the number of contexts in an expected manner (i.e., the entropy of the system is reduced). We also present how the incremental model can be used for various scene reasoning tasks