A Deep Incremental Boltzmann Machine for Modeling Context in Robots

Doğan, Fethiye Irmak; Çelikkanat, Hande; Kalkan, Sinan

doi:10.1109/icra.2018.8462925

Cited by 7 publications

(10 citation statements)

References 21 publications

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“…We also analyzed how the system behaves when a new context is added, as shown in Figure 5. Here, comparing with the result of Celikkanat et al [4] and Dogan et al [22] (iRBM and diBM models), we see that our system converges to the correct number of contexts and yields the same level of entropy for the system, where entropy is defined as follows (as in [4]):…”

Section: B Applying Cinet To Incremental Context Modelingsupporting

confidence: 75%

“…On an artificial dataset and a real dataset (SUN-RGBD scene dataset [21], we show that the network learns to add a new context when it decides necessary on encountering new scenes. Moreover, we compared our method with another incremental Latent Dirichlet Allocation method [4] and incremental Boltzmann Machines [22], and demonstrated that it performs better.…”

Section: B Contributionsmentioning

confidence: 99%

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CINet: A Learning Based Approach to Incremental Context Modeling in Robots

Doğan¹,

Bozcan²,

Çelık³

et al. 2017

Preprint

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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.

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Section: B Applying Cinet To Incremental Context Modelingsupporting

confidence: 75%

Section: B Contributionsmentioning

confidence: 99%

CINet: A Learning Based Approach to Incremental Context Modeling in Robots

Doğan¹,

Bozcan²,

Çelık³

et al. 2017

Preprint

Self Cite

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show abstract

“…These include deep Boltzmann machine 103,104,140 and DBNs. Dogan et al 105 presented an incremental and hierarchical model for task-related context modeling in robotics using RBM to model service robot’s context incrementally and achieved competitive performance compared with other techniques.…”

Section: Deep Neural Networkmentioning

confidence: 99%

A review of algorithms and techniques for image-based recognition and inference in mobile robotic systems

Tawiah

2020

International Journal of Advanced Robotic Systems

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Autonomous vehicles include driverless, self-driving and robotic cars, and other platforms capable of sensing and interacting with its environment and navigating without human help. On the other hand, semiautonomous vehicles achieve partial realization of autonomy with human intervention, for example, in driver-assisted vehicles. Autonomous vehicles first interact with their surrounding using mounted sensors. Typically, visual sensors are used to acquire images, and computer vision techniques, signal processing, machine learning, and other techniques are applied to acquire, process, and extract information. The control subsystem interprets sensory information to identify appropriate navigation path to its destination and action plan to carry out tasks. Feedbacks are also elicited from the environment to improve upon its behavior. To increase sensing accuracy, autonomous vehicles are equipped with many sensors [light detection and ranging (LiDARs), infrared, sonar, inertial measurement units, etc.], as well as communication subsystem. Autonomous vehicles face several challenges such as unknown environments, blind spots (unseen views), non-line-of-sight scenarios, poor performance of sensors due to weather conditions, sensor errors, false alarms, limited energy, limited computational resources, algorithmic complexity, human–machine communications, size, and weight constraints. To tackle these problems, several algorithmic approaches have been implemented covering design of sensors, processing, control, and navigation. The review seeks to provide up-to-date information on the requirements, algorithms, and main challenges in the use of machine vision–based techniques for navigation and control in autonomous vehicles. An application using land-based vehicle as an Internet of Thing-enabled platform for pedestrian detection and tracking is also presented.

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“…However, in real settings, the set of objects can grow in time. To overcome this problem, the input layer should be designed in an incremental manner, as suggested in [17,65,66].…”

Section: Limitations and Future Workmentioning

confidence: 99%

COSMO: Contextualized scene modeling with Boltzmann Machines

Bozcan

Kalkan

2019

Robotics and Autonomous Systems

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Scene modeling is very crucial for robots that need to perceive, reason about and manipulate the objects in their environments. In this paper, we adapt and extend Boltzmann Machines (BMs) for contextualized scene modeling. Although there are many models on the subject, ours is the first to bring together objects, relations, and affordances in a highly-capable generative model. For this end, we introduce a hybrid version of BMs where relations and affordances are incorporated with shared, tri-way connections into the model. Moreover, we introduce a dataset for relation estimation and modeling studies. We evaluate our method in comparison with several baselines on object estimation, out-ofcontext object detection, relation estimation, and affordance estimation tasks.Moreover, to illustrate the generative capability of the model, we show several example scenes that the model is able to generate, and demonstrate the benefits of the model on a humanoid robot. The code and the dataset are publicly made available at: https://github.com/bozcani/COSMO

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A Deep Incremental Boltzmann Machine for Modeling Context in Robots

Cited by 7 publications

References 21 publications

CINet: A Learning Based Approach to Incremental Context Modeling in Robots

CINet: A Learning Based Approach to Incremental Context Modeling in Robots

A review of algorithms and techniques for image-based recognition and inference in mobile robotic systems

COSMO: Contextualized scene modeling with Boltzmann Machines

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