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On Building Generalizable Learning Agents
It has been a long-standing goal in Artificial Intelligence (AI) to build machines that can solve tasks that humans can. Thanks to the recent rapid progress in data-driven methods, which train agents to solve tasks by learning from massive training data, there have been many successes in applying such learning approaches to handle and even solve a number of extremely challenging tasks, including image classification, language generation, robotics control, and several multi-player games. The key factor for all these data-driven successes is that the trained agents can generalize to test scenarios that are unseen during training. This generalization capability is the foundation for building any practical AI system. This thesis studies generalization, the fundamental challenge in AI, and proposes solutions to improve the generalization performances of learning agents in a variety of problems. We start by providing a formal formulation of the generalization problem in the context of reinforcement learning and proposing 4 principles within this formulation to guide the design of training techniques for improved generalization. We validate the effectiveness of our proposed principles by considering 4 different domains, from simple to complex, and developing domain-specific techniques following these principles. Particularly, we begin with the simplest domain, i.e., path-finding on graphs (Part I), and then consider visual navigation in a 3D world (Part II) and competition in complex multi-agent games (Part III), and lastly tackle some natural language processing tasks (Part IV). Empirical evidences demonstrate that the proposed principles can generally lead to much improved generalization performances in a wide range of problems
Semantic information for robot navigation: a survey
There is a growing trend in robotics for implementing behavioural mechanisms based on human psychology, such as the processes associated with thinking. Semantic knowledge has opened new paths in robot navigation, allowing a higher level of abstraction in the representation of information. In contrast with the early years, when navigation relied on geometric navigators that interpreted the environment as a series of accessible areas or later developments that led to the use of graph theory, semantic information has moved robot navigation one step further. This work presents a survey on the concepts, methodologies and techniques that allow including semantic information in robot navigation systems. The techniques involved have to deal with a range of tasks from modelling the environment and building a semantic map, to including methods to learn new concepts and the representation of the knowledge acquired, in many cases through interaction with users. As understanding the environment is essential to achieve high-level navigation, this paper reviews techniques for acquisition of semantic information, paying attention to the two main groups: human-assisted and autonomous techniques. Some state-of-the-art semantic knowledge representations are also studied, including ontologies, cognitive maps and semantic maps. All of this leads to a recent concept, semantic navigation, which integrates the previous topics to generate high-level navigation systems able to deal with real-world complex situationsThe research leading to these results has received funding from HEROITEA: Heterogeneous 480 Intelligent Multi-Robot Team for Assistance of Elderly People (RTI2018-095599-B-C21), funded by Spanish 481 Ministerio de Economía y Competitividad. The research leading to this work was also supported project "Robots sociales para estimulacón física, cognitiva y afectiva de mayores"; funded by the Spanish State Research Agency under grant 2019/00428/001. It is also funded by WASP-AI Sweden; and by Spanish project Robotic-Based Well-Being Monitoring and Coaching for Elderly People during Daily Life Activities (RTI2018-095599-A-C22)
Modeling Dynamic Environments with Scene Graph Memory
Embodied AI agents that search for objects in large environments such as
households often need to make efficient decisions by predicting object
locations based on partial information. We pose this as a new type of link
prediction problem: link prediction on partially observable dynamic graphs. Our
graph is a representation of a scene in which rooms and objects are nodes, and
their relationships are encoded in the edges; only parts of the changing graph
are known to the agent at each timestep. This partial observability poses a
challenge to existing link prediction approaches, which we address. We propose
a novel state representation -- Scene Graph Memory (SGM) -- with captures the
agent's accumulated set of observations, as well as a neural net architecture
called a Node Edge Predictor (NEP) that extracts information from the SGM to
search efficiently. We evaluate our method in the Dynamic House Simulator, a
new benchmark that creates diverse dynamic graphs following the semantic
patterns typically seen at homes, and show that NEP can be trained to predict
the locations of objects in a variety of environments with diverse object
movement dynamics, outperforming baselines both in terms of new scene
adaptability and overall accuracy. The codebase and more can be found at
https://www.scenegraphmemory.com
Towards generalization of semi-supervised place classification over generalized Voronoi graph
With the progress of human-robot interaction (HRI), the ability of a robot to perform high-level tasks in complex environments is fast becoming an essential requirement. To this end, it is desirable for a robot to understand the environment at both geometric and semantic levels. Therefore in recent years, research towards place classification has been gaining in popularity. After the era of heuristic and rule-based approaches, supervised learning algorithms have been extensively used for this purpose, showing satisfactory performance levels. However, most of those approaches have only been trained and tested in the same environments and thus impede a generalized solution. In this paper, we have proposed a semi-supervised place classification over a generalized Voronoi graph (SPCoGVG) which is a semi-supervised learning framework comprised of three techniques: support vector machine (SVM), conditional random field (CRF) and generalized Voronoi graph (GVG), in order to improve the generalizability. The inherent problem of training CRF with partially labeled data has been solved using a novel parameter estimation algorithm. The effectiveness of the proposed algorithm is validated through extensive analysis of data collected in international university environments. © 2013 Elsevier B.V. All rights reserved
Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective
Data-driven decision making is becoming an integral part of manufacturing
companies. Data is collected and commonly used to improve efficiency and
produce high quality items for the customers. IoT-based and other forms of
object tracking are an emerging tool for collecting movement data of
objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over
space and time. Movement data can provide valuable insights like process
bottlenecks, resource utilization, effective working time etc. that can be used
for decision making and improving efficiency.
Turning movement data into valuable information for industrial management and
decision making requires analysis methods. We refer to this process as movement
analytics. The purpose of this document is to review the current state of work
for movement analytics both in manufacturing and more broadly.
We survey relevant work from both a theoretical perspective and an
application perspective. From the theoretical perspective, we put an emphasis
on useful methods from two research areas: machine learning, and logic-based
knowledge representation. We also review their combinations in view of movement
analytics, and we discuss promising areas for future development and
application. Furthermore, we touch on constraint optimization.
From an application perspective, we review applications of these methods to
movement analytics in a general sense and across various industries. We also
describe currently available commercial off-the-shelf products for tracking in
manufacturing, and we overview main concepts of digital twins and their
applications
Implicit Obstacle Map-driven Indoor Navigation Model for Robust Obstacle Avoidance
Robust obstacle avoidance is one of the critical steps for successful
goal-driven indoor navigation tasks.Due to the obstacle missing in the visual
image and the possible missed detection issue, visual image-based obstacle
avoidance techniques still suffer from unsatisfactory robustness. To mitigate
it, in this paper, we propose a novel implicit obstacle map-driven indoor
navigation framework for robust obstacle avoidance, where an implicit obstacle
map is learned based on the historical trial-and-error experience rather than
the visual image. In order to further improve the navigation efficiency, a
non-local target memory aggregation module is designed to leverage a non-local
network to model the intrinsic relationship between the target semantic and the
target orientation clues during the navigation process so as to mine the most
target-correlated object clues for the navigation decision. Extensive
experimental results on AI2-Thor and RoboTHOR benchmarks verify the excellent
obstacle avoidance and navigation efficiency of our proposed method. The core
source code is available at https://github.com/xwaiyy123/object-navigation.Comment: 9 pages, 7 figures, 43 references. This paper has been accepted for
ACM MM 202
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