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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Achieving Imitation-Based Learning for a Humanoid Robot by Evolutionary Computation

Chung, Chi-Hsiu 29 July 2009 (has links)
This thesis presents an imitation-based methodology, also a simple and easy way, for a service robot to learn the behaviors demonstrated by the user. With this proposed method, a robot can learn human behavior through observation. Inspired by the concept of biological learning, this learning model is initiated when facing a new learning event. A series of experiments are conducted to use a humanoid robot as a platform to implement the proposed algorithm. Discussions are made of how the robot generates a complete behavior sequences performed by its demonstrator. Because it is time consuming for a robot to go through the whole process of learning, we thus propose a decomposed learning method to enhance the learning performance, that is, based on the past learning information, the robot can skip learning again the behaviors already known. For simple robot behaviors, a hierarchical evolutionary mechanism is developed to evolve the complete behavior trajectories. For complex behaviors sequences, different ways are used to tackle the scalability problem, including decomposing the overall task into several sub-tasks, exploiting behavior information recorded previously, and constructing a new strategy to maintain population diversity. To verify our approach, a different series of experiments have been conducted. The results show that our imitation-based approach is a natural way to teach the robot new behaviors. This evolutionary mechanism successfully enables a humanoid robot to perform the behavior sequences it learns.
2

Efficient Algorithms for Causal Linear Identification and Sequential Imitation Learning

Daniel R Kumor (12476310) 28 April 2022 (has links)
<p>Finding cause and effect relationships is one of the quintessential questions throughout many of the empirical sciences, AI, and Machine Learning. This dissertation develops graphical conditions and efficient algorithms for two problems, linear identification and imitation learning. For the first problem, it is well-known that correlation does not imply causation, so linear regression doesn’t necessarily find causal relations even in the limit of a large sample size. Over the past century, a plethora of methods has been developed for identifying interventional distributions given a combination of assumptions about the underlying mechanisms (e.g., linear functional dependence, causal diagram) and observational data. We characterize the computational complexity of several existing graphical criteria and develop new polynomial-time algorithms that subsume existing disparate efficient approaches. The proposed methods constitute the current state of the art in terms of polynomial-time identification coverage. In words, our methods have the capability of identifying the maximal set of structural coefficients when compared to any other efficient algorithms found in the literature.</p> <p>The second problem studied in the dissertation is Causal Sequential Imitation Learning, which is concerned with an agent that aims to learn a policy by observing an expert acting in the environment, and mimicking this expert's observed behavior. Sometimes, the agent (imitator) does not have access to the same set of observations or sensors as the expert, which gives rise to challenges in correctly interpreting expert actions. We develop necessary and sufficient conditions for the imitator to obtain identical performance to the expert in sequential settings given the domain’s causal diagram, and create a polynomial-time algorithm for finding the covariates to include when generating an imitating policy.</p> <p><br></p>
3

Imitation Learning of Whole-Body Grasps

Hsiao, Kaijen, Lozano-Pérez, Tomás 01 1900 (has links)
Humans often learn to manipulate objects by observing other people. In much the same way, robots can use imitation learning to pick up useful skills. A system is detailed here for using imitation learning to teach a robot to grasp objects using both hand and whole-body grasps, which use the arms and torso as well as hands. Demonstration grasp trajectories are created by teleoperating a simulated robot to pick up simulated objects. When presented with a new object, the system compares it against the objects in a stored database to pick a demonstrated grasp used on a similar object. Both objects are modeled as a combination of primitives—boxes, cylinders, and spheres—and by considering the new object to be a transformed version of the demonstration object, contact points are mapped from one object to the other. The best kinematically feasible grasp candidate is chosen with the aid of a grasp quality metric. To test the success of the chosen grasp, a full, collision-free grasp trajectory is found and an attempt is made to execute in the simulation. The implemented system successfully picks up 92 out of 100 randomly generated test objects in simulation. / Singapore-MIT Alliance (SMA)
4

Efficient supervision for robot learning via imitation, simulation, and adaptation

Wulfmeier, Markus January 2018 (has links)
In order to enable more widespread application of robots, we are required to reduce the human effort for the introduction of existing robotic platforms to new environments and tasks. In this thesis, we identify three complementary strategies to address this challenge, via the use of imitation learning, domain adaptation, and transfer learning based on simulations. The overall work strives to reduce the effort of generating training data by employing inexpensively obtainable labels and by transferring information between different domains with deviating underlying properties. Imitation learning enables a straightforward way for untrained personnel to teach robots to perform tasks by providing demonstrations, which represent a comparably inexpensive source of supervision. We develop a scalable approach to identify the preferences underlying demonstration data via the framework of inverse reinforcement learning. The method enables integration of the extracted preferences as cost maps into existing motion planning systems. We further incorporate prior domain knowledge and demonstrate that the approach outperforms the baselines including manually crafted cost functions. In addition to employing low-cost labels from demonstration, we investigate the adaptation of models to domains without available supervisory information. Specifically, the challenge of appearance changes in outdoor robotics such as illumination and weather shifts is addressed using an adversarial domain adaptation approach. A principal advantage of the method over prior work is the straightforwardness of adapting arbitrary, state-of-the-art neural network architectures. Finally, we demonstrate performance benefits of the method for semantic segmentation of drivable terrain. Our last contribution focuses on simulation to real world transfer learning, where the characteristic differences are not only regarding the visual appearance but the underlying system dynamics. Our work aims at parallel training in both systems and mutual guidance via auxiliary alignment rewards to accelerate training for real world systems. The approach is shown to outperform various baselines as well as a unilateral alignment variant.
5

Deep learning based approaches for imitation learning

Hussein, Ahmed January 2018 (has links)
Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation learning is to describe the desired behaviour by providing demonstrations rather than instructions. This enables agents to learn complex behaviours with general learning methods that require minimal task specific information. However, imitation learning faces many challenges. The objective of this thesis is to advance the state of the art in imitation learning by adopting deep learning methods to address two major challenges of learning from demonstrations. Firstly, representing the demonstrations in a manner that is adequate for learning. We propose novel Convolutional Neural Networks (CNN) based methods to automatically extract feature representations from raw visual demonstrations and learn to replicate the demonstrated behaviour. This alleviates the need for task specific feature extraction and provides a general learning process that is adequate for multiple problems. The second challenge is generalizing a policy over unseen situations in the training demonstrations. This is a common problem because demonstrations typically show the best way to perform a task and don't offer any information about recovering from suboptimal actions. Several methods are investigated to improve the agent's generalization ability based on its initial performance. Our contributions in this area are three fold. Firstly, we propose an active data aggregation method that queries the demonstrator in situations of low confidence. Secondly, we investigate combining learning from demonstrations and reinforcement learning. A deep reward shaping method is proposed that learns a potential reward function from demonstrations. Finally, memory architectures in deep neural networks are investigated to provide context to the agent when taking actions. Using recurrent neural networks addresses the dependency between the state-action sequences taken by the agent. The experiments are conducted in simulated environments on 2D and 3D navigation tasks that are learned from raw visual data, as well as a 2D soccer simulator. The proposed methods are compared to state of the art deep reinforcement learning methods. The results show that deep learning architectures can learn suitable representations from raw visual data and effectively map them to atomic actions. The proposed methods for addressing generalization show improvements over using supervised learning and reinforcement learning alone. The results are thoroughly analysed to identify the benefits of each approach and situations in which it is most suitable.
6

Training Robot Policies using External Memory Based Networks Via Imitation Learning

January 2018 (has links)
abstract: Recent advancements in external memory based neural networks have shown promise in solving tasks that require precise storage and retrieval of past information. Re- searchers have applied these models to a wide range of tasks that have algorithmic properties but have not applied these models to real-world robotic tasks. In this thesis, we present memory-augmented neural networks that synthesize robot navigation policies which a) encode long-term temporal dependencies b) make decisions in partially observed environments and c) quantify the uncertainty inherent in the task. We extract information about the temporal structure of a task via imitation learning from human demonstration and evaluate the performance of the models on control policies for a robot navigation task. Experiments are performed in partially observed environments in both simulation and the real world / Dissertation/Thesis / Masters Thesis Computer Science 2018
7

Deep Imitation Learning on Spatio-Temporal Data with Multiple Adversarial Agents Applied on Soccer

Lindström, Per January 2019 (has links)
Recently, the availability of high quality and high resolution spatio-temporal data has increased for many sports. This enabled deep analysis of player behaviour and game strategy. This thesis investigates the assumption that game strategy is latent information in tracking data from soccer games and the possibility of modelling player behaviour with deep imitation learning. A possible application would be to perform counterfactual analysis, and switch an observed player in a real sequence, with a simulated player to asses alternative scenarios. An imitation learning application is implemented using recurrent neural networks. It is shown that the application is able to learn individual player behaviour and perform rollouts on previously unseen sequences.
8

Imitation Learning based on Generative Adversarial Networks for Robot Path Planning

Yi, Xianyong 24 November 2020 (has links)
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.
9

Imitation Of Human Body Poses And Hand Gestures Using A Particle Based Fluidics Method

Tilki, Umut 01 October 2012 (has links) (PDF)
In this thesis, a new approach is developed, avoiding the correspondence problem caused by the difference in embodiment between imitator and demonstrator in imitation learning. In our work, the imitator is a fluidic system of dynamics totally different than the imitatee, which is a human performing hand gestures and human body postures. The fluidic system is composed of fluid particles, which are used for the discretization of the problem domain. In this work, we demonstrate the fluidics formation control so as to imitate by observation initially given human body poses and hand gestures. Our fluidic formation control is based on setting suitable parameters of Smoothed Particle Hydrodynamics (SPH), which is a particle based Lagrangian method, according to imitation learning. In the controller part, we developed three approaches: In the first one, we used Artificial Neural Networks (ANN) for training of the input-output pairs on the fluidic imitation system. We extracted shape based feature vectors for human hand gestures as inputs of the system and for output we took the fluid dynamics parameters. In the second approach, we employed the Principal Component Analysis (PCA) method for human hand gesture and human body pose classification and imitation. Lastly, we developed a region based controller which assigns the fluid parameters according to the human body poses and hand gestures. In this controller, our algorithm determines the best fitting ellipses on human body regions and human hand finger positions and maps ellipse parameters to the fluid parameters. The fluid parameters adjusted by the fluidics imitation controller are body force (f), density, stiffness coefficient and velocity of particles (V) so as to lead formations of fluidic swarms to human body poses and hand gestures.
10

Cognitive Interactive Robot Learning

Fonooni, Benjamin January 2014 (has links)
Building general purpose autonomous robots that suit a wide range of user-specified applications, requires a leap from today's task-specific machines to more flexible and general ones. To achieve this goal, one should move from traditional preprogrammed robots to learning robots that easily can acquire new skills. Learning from Demonstration (LfD) and Imitation Learning (IL), in which the robot learns by observing a human or robot tutor, are among the most popular learning techniques. Showing the robot how to perform a task is often more natural and intuitive than figuring out how to modify a complex control program. However, teaching robots new skills such that they can reproduce the acquired skills under any circumstances, on the right time and in an appropriate way, require good understanding of all challenges in the field. Studies of imitation learning in humans and animals show that several cognitive abilities are engaged to learn new skills correctly. The most remarkable ones are the ability to direct attention to important aspects of demonstrations, and adapting observed actions to the agents own body. Moreover, a clear understanding of the demonstrator's intentions and an ability to generalize to new situations are essential. Once learning is accomplished, various stimuli may trigger the cognitive system to execute new skills that have become part of the robot's repertoire. The goal of this thesis is to develop methods for learning from demonstration that mainly focus on understanding the tutor's intentions, and recognizing which elements of a demonstration need the robot's attention. An architecture containing required cognitive functions for learning and reproduction of high-level aspects of demonstrations is proposed. Several learning methods for directing the robot's attention and identifying relevant information are introduced. The architecture integrates motor actions with concepts, objects and environmental states to ensure correct reproduction of skills. Another major contribution of this thesis is methods to resolve ambiguities in demonstrations where the tutor's intentions are not clearly expressed and several demonstrations are required to infer intentions correctly. The provided solution is inspired by human memory models and priming mechanisms that give the robot clues that increase the probability of inferring intentions correctly. In addition to robot learning, the developed techniques are applied to a shared control system based on visual servoing guided behaviors and priming mechanisms. The architecture and learning methods are applied and evaluated in several real world scenarios that require clear understanding of intentions in the demonstrations. Finally, the developed learning methods are compared, and conditions where each of them has better applicability are discussed. / Att bygga autonoma robotar som passar ett stort antal olika användardefinierade applikationer kräver ett språng från dagens specialiserade maskiner till mer flexibla lösningar. För att nå detta mål, bör man övergå från traditionella förprogrammerade robotar till robotar som själva kan lära sig nya färdigheter. Learning from Demonstration (LfD) och Imitation Learning (IL), där roboten lär sig genom att observera en människa eller en annan robot, är bland de mest populära inlärningsteknikerna. Att visa roboten hur den ska utföra en uppgift är ofta mer naturligt och intuitivt än att modifiera ett komplicerat styrprogram. Men att lära robotar nya färdigheter så att de kan reproducera dem under nya yttre förhållanden, på rätt tid och på ett lämpligt sätt, kräver god förståelse för alla utmaningar inom området. Studier av LfD och IL hos människor och djur visar att flera kognitiva förmågor är inblandade för att lära sig nya färdigheter på rätt sätt. De mest anmärkningsvärda är förmågan att rikta uppmärksamheten på de relevanta aspekterna i en demonstration, och förmågan att anpassa observerade rörelser till robotens egen kropp. Dessutom är det viktigt att ha en klar förståelse av lärarens avsikter, och att ha förmågan att kunna generalisera dem till nya situationer. När en inlärningsfas är slutförd kan stimuli trigga det kognitiva systemet att utföra de nya färdigheter som blivit en del av robotens repertoar. Målet med denna avhandling är att utveckla metoder för LfD som huvudsakligen fokuserar på att förstå lärarens intentioner, och vilka delar av en demonstration som ska ha robotens uppmärksamhet. Den föreslagna arkitekturen innehåller de kognitiva funktioner som behövs för lärande och återgivning av högnivåaspekter av demonstrationer. Flera inlärningsmetoder för att rikta robotens uppmärksamhet och identifiera relevant information föreslås. Arkitekturen integrerar motorkommandon med begrepp, föremål och omgivningens tillstånd för att säkerställa korrekt återgivning av beteenden. Ett annat huvudresultat i denna avhandling rör metoder för att lösa tvetydigheter i demonstrationer, där lärarens intentioner inte är klart uttryckta och flera demonstrationer är nödvändiga för att kunna förutsäga intentioner på ett korrekt sätt. De utvecklade lösningarna är inspirerade av modeller av människors minne, och en primingmekanism används för att ge roboten ledtrådar som kan öka sannolikheten för att intentioner förutsägs på ett korrekt sätt. De utvecklade teknikerna har, i tillägg till robotinlärning, använts i ett halvautomatiskt system (shared control) baserat på visuellt guidade beteenden och primingmekanismer. Arkitekturen och inlärningsteknikerna tillämpas och utvärderas i flera verkliga scenarion som kräver en tydlig förståelse av mänskliga intentioner i demonstrationerna. Slutligen jämförs de utvecklade inlärningsmetoderna, och deras applicerbarhet under olika förhållanden diskuteras. / INTRO

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