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Guilherme Maeda

Bio: Guilherme Maeda is an academic researcher from Technische Universität Darmstadt. The author has contributed to research in topics: Robot & Robotic arm. The author has an hindex of 18, co-authored 52 publications receiving 965 citations. Previous affiliations of Guilherme Maeda include Tokyo Institute of Technology & University of Sydney.


Papers
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Journal ArticleDOI
TL;DR: An interaction learning method for collaborative and assistive robots based on movement primitives that allows for both action recognition and human–robot movement coordination and is scalable in relation to the number of tasks.
Abstract: This paper proposes an interaction learning method for collaborative and assistive robots based on movement primitives. The method allows for both action recognition and human---robot movement coordination. It uses imitation learning to construct a mixture model of human---robot interaction primitives. This probabilistic model allows the assistive trajectory of the robot to be inferred from human observations. The method is scalable in relation to the number of tasks and can learn nonlinear correlations between the trajectories that describe the human---robot interaction. We evaluated the method experimentally with a lightweight robot arm in a variety of assistive scenarios, including the coordinated handover of a bottle to a human, and the collaborative assembly of a toolbox. Potential applications of the method are personal caregiver robots, control of intelligent prosthetic devices, and robot coworkers in factories.

166 citations

Proceedings ArticleDOI
26 May 2015
TL;DR: A Mixture of Interaction Primitives is proposed to learn multiple interaction patterns from unlabeled demonstrations to overcome the limitation of this framework to represent and generalize a single interaction pattern.
Abstract: Robots that interact with humans must learn to not only adapt to different human partners but also to new interactions. Such a form of learning can be achieved by demonstrations and imitation. A recently introduced method to learn interactions from demonstrations is the framework of Interaction Primitives. While this framework is limited to represent and generalize a single interaction pattern, in practice, interactions between a human and a robot can consist of many different patterns. To overcome this limitation this paper proposes a Mixture of Interaction Primitives to learn multiple interaction patterns from unlabeled demonstrations. Specifically the proposed method uses Gaussian Mixture Models of Interaction Primitives to model nonlinear correlations between the movements of the different agents. We validate our algorithm with two experiments involving interactive tasks between a human and a lightweight robotic arm. In the first, we compare our proposed method with conventional Interaction Primitives in a toy problem scenario where the robot and the human are not linearly correlated. In the second, we present a proof-of-concept experiment where the robot assists a human in assembling a box.

101 citations

Proceedings ArticleDOI
18 Nov 2014
TL;DR: This paper introduces the use of Probabilistic Movement Primitives (ProMPs) to devise an interaction method that both recognizes the action of a human and generates the appropriate movement primitive of the robot assistant.
Abstract: This paper proposes a probabilistic framework based on movement primitives for robots that work in collaboration with a human coworker. Since the human coworker can execute a variety of unforeseen tasks a requirement of our system is that the robot assistant must be able to adapt and learn new skills on-demand, without the need of an expert programmer. Thus, this paper leverages on the framework of imitation learning and its application to human-robot interaction using the concept of Interaction Primitives (IPs). We introduce the use of Probabilistic Movement Primitives (ProMPs) to devise an interaction method that both recognizes the action of a human and generates the appropriate movement primitive of the robot assistant. We evaluate our method on experiments using a lightweight arm interacting with a human partner and also using motion capture trajectories of two humans assembling a box. The advantages of ProMPs in relation to the original formulation for interaction are exposed and compared.

90 citations

Proceedings ArticleDOI
01 Sep 2017
TL;DR: This paper uses postural assessment techniques, and a personalized human kinematic model, to optimize the model body posture to fulfill a task while avoiding uncomfortable or unsafe postures, and derives a robotic behavior that leads the worker towards that improved posture.
Abstract: In human-robot collaboration the robot's behavior impacts the worker's safety, comfort and acceptance of the robotic system. In this paper we address the problem of how to improve the worker's posture during human-robot collaboration. Using postural assessment techniques, and a personalized human kinematic model, we optimize the model body posture to fulfill a task while avoiding uncomfortable or unsafe postures. We then derive a robotic behavior that leads the worker towards that improved posture. We validate our approach in an experiment involving a joint task with 39 human subjects and a Baxter torso-humanoid robot.

77 citations

Journal ArticleDOI
TL;DR: The method is fundamentally different from approaches based on Dynamic Time Warping that must rely on a consistent stream of measurements at runtime, and can achieve phase estimation, action recognition and robot trajectory coordination using a single probabilistic representation.
Abstract: This paper proposes a method to achieve fast and fluid human–robot interaction by estimating the progress of the movement of the human. The method allows the progress, also referred to as the phase of the movement, to be estimated even when observations of the human are partial and occluded; a problem typically found when using motion capture systems in cluttered environments. By leveraging on the framework of Interaction Probabilistic Movement Primitives, phase estimation makes it possible to classify the human action, and to generate a corresponding robot trajectory before the human finishes his/her movement. The method is therefore suited for semi-autonomous robots acting as assistants and coworkers. Since observations may be sparse, our method is based on computing the probability of different phase candidates to find the phase that best aligns the Interaction Probabilistic Movement Primitives with the current observations. The method is fundamentally different from approaches based on Dynamic Time Warping that must rely on a consistent stream of measurements at runtime. The resulting framework can achieve phase estimation, action recognition and robot trajectory coordination using a single probabilistic representation. We evaluated the method using a seven-degree-of-freedom lightweight robot arm equipped with a five-finger hand in single and multi-task collaborative experiments. We compare the accuracy achieved by phase estimation with our previous method based on dynamic time warping.

72 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

01 Jun 2005

3,154 citations

Journal ArticleDOI
TL;DR: Digital Control Of Dynamic Systems This well-respected, market-leading text discusses the use of digital computers in the real-time control of dynamic systems with an emphasis on the design of digital controls that achieve good dynamic response and small errors while using signals that are sampled in time and quantized in amplitude.
Abstract: Digital Control Of Dynamic Systems This well-respected, market-leading text discusses the use of digital computers in the real-time control of dynamic systems. The emphasis is on the design of digital controls that achieve good dynamic response and small errors while using signals that are sampled in time and quantized in amplitude. Digital Control of Dynamic Systems (3rd Edition): Franklin ... This well-respected, market-leading text discusses the use of digital computers in the real-time control of dynamic systems. The emphasis is on the design of digital controls that achieve good dynamic response and small errors while using signals that are sampled in time and quantized in amplitude. Digital Control of Dynamic Systems: Gene F. Franklin ... Digital Control of Dynamic Systems, 2nd Edition. Gene F. Franklin, Stanford University. J. David Powell, Stanford University Digital Control of Dynamic Systems, 2nd Edition Pearson This well-respected work discusses the use of digital computers in the real-time control of dynamic systems. The emphasis is on the design of digital controls that achieve good dynamic response and small errors while using signals that are sampled in time and quantized in amplitude. MATLAB statements and problems are thoroughly and carefully integrated throughout the book to offer readers a complete design picture. Digital Control of Dynamic Systems, 3rd Edition ... Digital control of dynamic systems | Gene F. Franklin, J. David Powell, Michael L. Workman | download | B–OK. Download books for free. Find books Digital control of dynamic systems | Gene F. Franklin, J ... Abstract This well-respected work discusses the use of digital computers in the real-time control of dynamic systems. The emphasis is on the design of digital controls that achieve good dynamic... (PDF) Digital Control of Dynamic Systems Digital Control of Dynamic Systems, Addison.pdf There is document Digital Control of Dynamic Systems, Addison.pdfavailable here for reading and downloading. Use the download button below or simple online reader. The file extension PDFand ranks to the Documentscategory. Digital Control of Dynamic Systems, Addison.pdf Download ... Automatic control is the science that develops techniques to steer, guide, control dynamic systems. These systems are built by humans and must perform a specific task. Examples of such dynamic systems are found in biology, physics, robotics, finance, etc. Digital Control means that the control laws are implemented in a digital device, such as a microcontroller or a microprocessor. Introduction to Digital Control of Dynamic Systems And ... The discussions are clear, nomenclature is not hard to follow and there are plenty of worked examples. The book covers discretization effects and design by emulation (i.e. design of continuous-time control system followed by discretization before implementation) which are not to be found on every book on digital control. Amazon.com: Customer reviews: Digital Control of Dynamic ... Find helpful customer reviews and review ratings for Digital Control of Dynamic Systems (3rd Edition) at Amazon.com. Read honest and unbiased product reviews from our users. Amazon.com: Customer reviews: Digital Control of Dynamic ... 1.1.2 Digital control Digital control systems employ a computer as a fundamental component in the controller. The computer typically receives a measurement of the controlled variable, also often receives the reference input, and produces its output using an algorithm. Introduction to Applied Digital Control From the Back Cover This well-respected, marketleading text discusses the use of digital computers in the real-time control of dynamic systems. The emphasis is on the design of digital controls that achieve good dynamic response and small errors while using signals that are sampled in time and quantized in amplitude. Digital Control of Dynamic Systems (3rd Edition) Test Bank `Among the advantages of digital logic for control are the increased flexibility `of the control programs and the decision-making or logic capability of digital `systems, which can be combined with the dynamic control function to meet `other system requirements. `The digital controls studied in this book are for closed-loop (feedback) Every day, eBookDaily adds three new free Kindle books to several different genres, such as Nonfiction, Business & Investing, Mystery & Thriller, Romance, Teens & Young Adult, Children's Books, and others.

902 citations

Journal ArticleDOI
TL;DR: An extensive review on human–robot collaboration in industrial environment is provided, with specific focus on issues related to physical and cognitive interaction, and the commercially available solutions are presented.

632 citations