Robot path planning and dynamic obstacle avoidance are defined as a problem that robots plan a feasible path from a given starting point to a destination point in a nonlinear dynamic environment, and safely bypass dynamic obstacles to the destination with minimal deviation from the trajectory. Path planning is a typical sequential decision-making problem. Dynamic local observable environment requires real-time and adaptive decision-making systems. It is an innovation for the robot to learn the policy directly from demonstration trajectories to adapt to similar state spaces that may appear in the future. We aim to develop a method for directly learning navigation behavior from demonstration trajectories without defining the environment and attention models, by using the concepts of Generative Adversarial Imitation Learning (GAIL) and Sequence Generative Adversarial Network (SeqGAN). The proposed SeqGAIL model in this thesis allows the robot to reproduce the desired behavior in different situations. In which, an adversarial net is established, and the Feature Counts Errors reduction is utilized as the forcing objective for the Generator. The refinement measure is taken to solve the instability problem. In addition, we proposed to use the Rapidly-exploring Random Tree* (RRT*) with pre-trained weights to generate adequate demonstration trajectories in dynamic environment as the training data, and this idea can effectively overcome the difficulty of acquiring huge training data.

Macroeconomic forecasting is a classic problem, today most often modeled using time series analysis. Few attempts have been made using machine learning methods, and even fewer incorporating unconventional data, such as that from social media. In this thesis, a Generative Adversarial Network (GAN) is used to predict U.S. unemployment, beating the ARIMA benchmark on all horizons. Furthermore, attempts at using Twitter data and the Natural Language Processing (NLP) model DistilBERT are performed. While these attempts do not beat the benchmark, they do show promising results with predictive power. The models are also tested at predicting the U.S. stock index S&P 500. For these models, the Twitter data does improve the accuracy and shows the potential of social media data when predicting a more erratic index with less seasonality that is more responsive to current trends in public discourse. The results also show that Twitter data can be used to predict trends in both unemployment and the S&P 500 index. This sets the stage for further research into NLP-GAN models for macroeconomic predictions using social media data. / Makroekonomiska prognoser är sedan länge en svår utmaning. Idag löses de oftast med tidsserieanalys och få försök har gjorts med maskininlärning. I denna uppsats används ett generativt motstridande nätverk (GAN) för att förutspå amerikansk arbetslöshet, med resultat som slår samtliga riktmärken satta av en ARIMA. Ett försök görs också till att använda data från Twitter och den datorlingvistiska (NLP) modellen DistilBERT. Dessa modeller slår inte riktmärkena men visar lovande resultat. Modellerna testas vidare på det amerikanska börsindexet S&P 500. För dessa modeller förbättrade Twitterdata resultaten vilket visar på den potential data från sociala medier har när de appliceras på mer oregelbunda index, utan tydligt säsongsberoende och som är mer känsliga för trender i det offentliga samtalet. Resultaten visar på att Twitterdata kan användas för att hitta trender i både amerikansk arbetslöshet och S&P 500 indexet. Detta lägger grunden för fortsatt forskning inom NLP-GAN modeller för makroekonomiska prognoser baserade på data från sociala medier.

<p>3D scene reconstruction based on single-view images is an ill-posed problem since most 3D information has been lost during the projection process from the 3D world coordinates to the 2D pixel coordinates. To estimate the portion of an object from a single-view requires either the use of priori information such as the geometric shape of the object, or training based techniques that learn from existing portion sizes distribution. In this thesis, we present a single-view based technique for food portion size estimation.</p><p><br></p> <p>Dietary assessment, the process of determining what someone eats during the course of a day, provides valuable insights for mounting intervention programs for prevention of many chronic diseases such as cancer, diabetes and heart diseases. Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. We have developed a mobile dietary assessment system, the Technology Assisted Dietary Assessment^TM (TADA^TM) system to automatically determine the food types and energy consumed by a user using image analysis techniques.</p><p><br></p><p>In this thesis we focus on the use of a single image for food portion size estimation to reduce a user’s burden from having to take multiple images of their meal. We define portion size estimation as the process of determining how much food (or food energy/nutrient) is present in the food image. In addition to estimating food energy/nutrient, food portion estimation could also be estimating food volumes (in cm³) or weights (in grams), as they are directly related to food energy/nutrient. Food portion estimation is a challenging problem as food preparation and consumption process can pose large variations in food shapes and appearances.</p><p><br></p><p>As single-view based 3D reconstruction is in general an ill-posed problem, we investigate the use of geometric models such as the shape of a container that can help to partially recover 3D parameters of food items in the scene. We compare the performance of portion estimation technique based on 3D geometric models to techniques using depth maps. We have shown that more accurate estimation can be obtained by using geometric models for objects whose 3D shape are well defined. To further improve the food estimation accuracy we investigate the use of food portions co-occurrence patterns. The food portion co-occurrence patterns can be estimated from food image dataset we collected from dietary studies using the mobile Food Record^TM (mFR^TM) system we developed. Co-occurrence patterns is used as prior knowledge to refine portion estimation results. We have been shown that the portion estimation accuracy has been improved when incorporating the co-occurrence patterns as contextual information.</p><p><br></p><p>In addition to food portion estimation techniques that are based on geometric models, we also investigate the use deep learning approach. In the geometric model based approach, we have focused on estimation food volumes. However, food volumes are not the final results that directly show food energy/nutrient consumed. Therefore, instead of developing food portion estimation techniques that lead to an intermediate results (food volumes), we present a food portion estimation method to directly estimate food energy (kilocalories) from food images using Generative Adversarial Networks (GANs). We introduce the concept of an “energy distribution” for each food image. To train the GAN, we design a food image dataset based on ground truth food labels and segmentation masks for each food image as well as energy information associated with the food image. Our goal is to learn the mapping of the food image to the food energy. We then estimate food energy based on the estimated energy distribution image. Based on the estimated energy distribution image, we use a Convolutional Neural Networks (CNN) to estimate the numeric values of food energy presented in the eating scene.</p><p><br></p>