Machine Learning in Robotic Navigation:Deep Visual Localization and Adaptive Control

The work conducted in this thesis contributes to the robotic navigation field by focusing on different machine learning solutions: supervised learning with (deep) neural networks, unsupervised learning, and reinforcement learning.First, we propose a semi-supervised machine learning approach that can dynamically update the robot controller's parameters using situational analysis through feature extraction and unsupervised clustering. The results show that the robot can adapt to the changes in its surroundings, resulting in a thirty percent improvement in navigation speed and stability.Then, we train multiple deep neural networks for estimating the robot's position in the environment using ground truth information provided by a classical localization and mapping approach. We prepare two image-based localization datasets in 3D simulation and compare the results of a traditional multilayer perceptron, a stacked denoising autoencoder, and a convolutional neural network (CNN). The experiment results show that our proposed inception based CNNs without pooling layers perform very well in all the environments. Finally, we propose a two-stage learning framework for visual navigation in which the experience of the agent during exploration of one goal is shared to learn to navigate to other goals. The multi-goal Q-function learns to traverse the environment by using the provided discretized map. Transfer learning is applied to the multi-goal Q-function from a maze structure to a 2D simulator and is finally deployed in a 3D simulator where the robot uses the estimated locations from the position estimator deep CNNs. The results show a significant improvement when multi-goal reinforcement learning is used

A Deep Convolutional Neural Network for Location Recognition and Geometry based Information

In this paper we propose a new approach to Deep Neural Networks (DNNs) based on the particular needs of navigation tasks. To investigate these needs we created a labeled image dataset of a test environment and we compare classical computer vision approaches with the state of the art in image classification. Based on these results we have developed a new DNN architecture that outperforms previous architectures in recognizing locations, relying on the geometrical features of the images. In particular we show the negative effects of scale, rotation, and position invariance properties of the current state of the art DNNs on the task. We finally show the results of our proposed architecture that preserves the geometrical properties. Our experiments show that our method outperforms the state of the art image classification networks in recognizing locations

Supporting medication intake of the elderly with robot technology: Poster and demonstration

Medication intake can prove a complicated task for the elderly. Since roughly 50% of all prescribed medication is taken incorrectly (MacLaughlin, et al., 2005), simplification of this task might have beneficial effects on this group’s general health and society’s healthcare costs. In response, Assistobot Corporation has commissioned the present study alongside its development of an assistive robot for the elderly, called RITA (the Reliable Interactive Table Assistant). The aim of this study was twofold: Firstly to develop a robot interface to assist the elderly with their medication intake. Secondly, to investigate whether the target group is willing to accept medication intake assistance from a robot. In order to fully map the process involved and so prepare for the initial stages of development, caregivers were interviewed about the medication intake task. The responses were analyzed and served to guide the development of the robot interface. The caregivers indicated that it was important for them to check whether the elderly actually took their medication. Wireframes were created before the actual interface was developed. A focus group was asked to provide feedback on the clarity of the design, and whether it met their requirements. Our test group found that the font size should be increased for optimal utility. The interface was developed in HTML5 and tested in a user study which consisted of a usability test and the post-study Usability Questionnaire (PSSUQ) (Lewis, 1992). The questionnaire was extended with an acceptance questionnaire to investigate whether elderly would accept a robot to assist them with their medication intake. This questionnaire was based on the ALMERE-model (Heerink, Krose, Evers, & Wielinga, 2010) (Xu, et al., 2014). The usability test showed that the majority of participants in this study (17 out of 19) were able to take their medication with assistance of the interface. However, they found it difficult to work certain interface settings, such as those concerning the notifications interval or their pharmacy's contact details. Furthermore, on a five-point Likert scale, the PSSUQ resulted in a mean score of 3.9 (between 'Neutral' and 'Agree'); the Robot Acceptance Questionnaire scored a 3.5. Along with the results of the usability test, the questionnaire findings indicate that the interface could be used by the elderly for assistance with the medication intake task and that they are willing to accept assistance of a robot with this task in the future. Heerink, M., Krose, B., Evers, V., & Wielinga, B. (2010). Assessing acceptance of assistive social agent technology by older adults: the Almere model. International Journal of Social Robotics, 361-375. MacLaughlin, E. J., Raehl, C. L., Treadway, A. K., Sterling, T. L., Zoller, D. P., & Bond, C. A. (2005). Assessing medication adherence in the elderly. Drugs & Aging, 231-255. Lewis, J. R. (1992). Psychometric evaluation of the post-study system usability questionnaire: The PSSUQ. Proceedings of the human factors society, 1259-1263. Xu, Q., Ng, J., Tan, O., Huang, Z., Tay, B., & Park, T. (2014). Methodological Issues in Scenario-Based Evaluation of Human-Robot interaction. International journal of social robotics

A Deep Convolutional Neural Network for Location Recognition and Geometry based Information

In this paper we propose a new approach to Deep Neural Networks (DNNs) based on the particular needs of navigation tasks. To investigate these needs we created a labeled image dataset of a test environment and we compare classical computer vision approaches with the state of the art in image classification. Based on these results we have developed a new DNN architecture that outperforms previous architectures in recognizing locations, relying on the geometrical features of the images. In particular we show the negative effects of scale, rotation, and position invariance properties of the current state of the art DNNs on the task. We finally show the results of our proposed architecture that preserves the geometrical properties. Our experiments show that our method outperforms the state of the art image classification networks in recognizing locations