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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Christian Friedrich Viktor Zielke Marc Toussaint Armin Lechler Alexander Verl Institute of Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart Germany Institute of Machine Learning and Robotics University Stuttgart Stuttgart Germany

In order to enable robot systems to solve complex tasks autonomously, it is essential to model the environment in a sufficient way. This work describes a new approach to create a consistent environment model, using next-best-view planning which combines the optimization of the space exploration and the object recognition task. The approach is designed for the automation of maintenance tasks, which is a challenging task for autonomous robotic systems. After a general overview of the developed system, we explain the theoretic foundations for building a consistent environment model which can be used for manipulation planning. The concept is validated by means of application-oriented examples.

关键词： Solid modeling Maintenance engineering Automation Robot sensing systems Planning

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Policy search in reproducing kernel Hilbert space 25

Policy search in reproducing kernel Hilbert space

引用

25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Vien, Ngo Anh Englert, Peter Toussaint, Marc Machine Learning and Robotics Lab University of Stuttgart Germany

Modeling policies in reproducing kernel Hilbert space (RKHS) renders policy gradient reinforcement learning algorithms non-parametric. As a result, the policies become very flexible and have a rich representational potential without a predefined set of features. However, their performances might be either non-covariant under reparameterization of the chosen kernel, or very sensitive to step-size selection. In this paper, we propose to use a general framework to derive a new RKHS policy search technique. The new derivation leads to both a natural RKHS actor-critic algorithm and a RKHS expectation maximization (EM) policy search algorithm. Further, we show that kernelization enables us to learn in partially observable (POMDP) tasks which is considered daunting for parametric approaches. Via sparsification, a small set of "support vectors" representing the history is shown to be effectively discovered. For evaluations, we use three simulated (PO)MDP reinforcement learning tasks, and a simulated PR2's robotic manipulation task. The results demonstrate the effectiveness of the new RKHS policy search framework in comparison to plain RKHS actor-critic, episodic natural actor-critic, plain actor-critic, and PoWER approaches.

关键词： Reinforcement learning

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Opening a lockbox through physical exploration

Opening a lockbox through physical exploration

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IEEE-RAS International Conference on Humanoid Robots

作者： Manuel Baum Matthew Bernstein Roberto Martin-Martin Sebastian Höfer Johannes Kulick Marc Toussaint Alex Kacelnik Oliver Brock Robotics and Biology Lab (RBO) Technische Universität Berlin Machine Learning and Robotics Lab (MLR) Universität Stuttgart Department of Zoology Oxford University

ISBN: (纸本)9781538646793

How can we close the gap between animals and robots when it comes to intelligently interacting with the environment? On our quest for answers, we have investigated the problem of physically exploring complex mechanical puzzles, called lockboxes. Biologists have discovered that cockatoos are intrinsically motivated to explore and solve such problems through physical explorative behavior. In this work, we study how different strategies shape the robots' exploration, given basic perception-action skills. Our evaluation highlights the influence of different statistical priors on the performance of the exploration strategies, showing that not only a range of computational methods, but also a range of priors could explain different exploration behaviors. We carry out our study of exploration strategies both in simulation and on two robot platforms. This first step towards a fully integrated real-world system allowed us to identify and remove limitations of our prior theoretical work on cross-entropy-based exploration when applied to complex realistic scenarios. In this paper we propose novel variants of this strategy and our experiments verify that the cross-entropy method performs well on a physical lockbox analogue of the cockatoo apparatus, and can generalize to lockboxes of different properties.

关键词： Robot sensing systems Entropy Kinematics Robot kinematics Planning

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Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints

arXiv

引用

arXiv 2017年

作者： Yildirim, Ilker Gerstenberg, Tobias Saeed, Basil Toussaint, Marc Tenenbaum, Joshua B. Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA United States Machine Learning and Robotics Lab University of Stuttgart Germany

In this paper, we present a new task that investigates how people interact with and make judgments about towers of blocks. In Experiment 1, participants in the lab solved a series of problems in which they had to re-configure three blocks from an initial to a final configuration. We recorded whether they used one hand or two hands to do so. In Experiment 2, we asked participants online to judge whether they think the person in the lab used one or two hands. The results revealed a close correspondence between participants' actions in the lab, and the mental simulations of participants online. To explain participants' actions and mental simulations, we develop a model that plans over a symbolic representation of the situation, executes the plan using a geometric solver, and checks the plan's feasibility by taking into account the physical constraints of the scene. Our model explains participants' actions and judgments to a high degree of quantitative accuracy. Copyright © 2017, The Authors. All rights reserved.

关键词： Geometry

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Multi-bound tree search for logic-geometric programming in cooperative manipulation domains

Multi-bound tree search for logic-geometric programming in c...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Marc Toussaint Manuel Lopes Machine Learning and Robotics Lab University of Stuttgart Germany INESC-ID Instituto Superior Técnico Universide de Lisboa Portugal

Joint symbolic and geometric planning is one of the core challenges in robotics. We address the problem of multi-agent cooperative manipulation, where we aim for jointly optimal paths for all agents and over the full manipulation sequence. This joint optimization problem can be framed as a logic-geometric program. Existing solvers lack several features (such as consistently handling kinematic switches) and efficiency to handle the cooperative manipulation domain. We propose a new approximate solver scheme, combining ideas from branch-and-bound and MCTS and exploiting multiple levels of bounds to better direct the search. We demonstrate the method in a scenario where a Baxter robot needs to help a human to reach for objects.

关键词： Kinematics Optimization Planning Aerospace electronics Programming Robots Search problems

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Fast and continuous foothold adaptation for dynamic locomotion through CNNs

arXiv

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arXiv 2018年

作者： Villarreal, Octavio Barasuol, Victor Camurri, Marco Franceschi, Luca Focchi, Michele Pontil, Massimiliano Caldwell, Darwin G. Semini, Claudio Dynamic Legged Systems lab Istituto Italiano di Tecnologia Via Morego 30 Genoa16163 Italy Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Via Morego 30 Genoa16163 Italy Department of Advanced Robotics Istituto Italiano di Tecnologia Via Morego 30 Genoa16163 Italy Oxford Robotics Institute University of Oxford 23 Banbury Rd OxfordOX2 6NN United Kingdom

—Legged robots can outperform wheeled machines for most navigation tasks across unknown and rough terrains. For such tasks, visual feedback is a fundamental asset to provide robots with terrain-awareness. However, robust dynamic locomotion on difficult terrains with real-time performance guarantees remains a challenge. We present here a real-time, dynamic foothold adaptation strategy based on visual feedback. Our method adjusts the landing position of the feet in a fully reactive manner, using only on-board computers and sensors. The correction is computed and executed continuously along the swing phase trajectory of each leg. To efficiently adapt the landing position, we implement a self-supervised foothold classifier based on a Convolutional Neural Network (CNN). Our method results in an up to 200 times faster computation with respect to the full-blown heuristics. Our goal is to react to visual stimuli from the environment, bridging the gap between blind reactive locomotion and purely vision-based planning strategies. We assess the performance of our method on the dynamic quadruped robot HyQ, executing static and dynamic gaits (at speeds up to 0.5 m/s) in both simulated and real scenarios;the benefit of safe foothold adaptation is clearly demonstrated by the overall robot behavior. Copyright © 2018, The Authors. All rights reserved.

关键词： Robots

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Discovery of Complex Anomalous Patterns of Sexual Violence in El Salvador

arXiv

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arXiv 2017年

作者： De-Arteaga, Maria Dubrawski, Artur Auton Lab Carnegie Mellon University Machine Learning Department Carnegie Mellon University Heinz College Carnegie Mellon University Robotics Institute Carnegie Mellon University

When sexual violence is a product of organized crime or social imaginary, the links between sexual violence episodes can be understood as a latent structure. With this assumption in place, we can use data science to uncover complex patterns. In this paper we focus on the use of data mining techniques to unveil complex anomalous spatiotemporal patterns of sexual violence. We illustrate their use by analyzing all reported rapes in El Salvador over a period of nine years. Through our analysis, we are able to provide evidence of phenomena that, to the best of our knowledge, have not been previously reported in literature. We devote special attention to a pattern we discover in the East, where underage victims report their boyfriends as perpetrators at anomalously high rates. Finally, we explain how such analyzes could be conducted in real-time, enabling early detection of emerging patterns to allow law enforcement agencies and policy makers to react accordingly. Copyright © 2017, The Authors. All rights reserved.

关键词： Anomaly detection

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Relational Activity Processes for Modeling Concurrent Cooperation

Relational Activity Processes for Modeling Concurrent Cooper...

引用

IEEE International Conference on robotics and Automation

作者： Marc Toussaint Thibaut Munzer Yoan Mollard Li Yang Wu Ngo Anh Vien Manuel Lopes Machine Learning and Robotics Lab University of Stuttgart Germany

ISBN: (纸本)9781467380270

In human-robot collaboration, multi-agent domains, or single-robot manipulation with multiple end-effectors, the activities of the involved parties are naturally concurrent. Such domains are also naturally relational as they involve objects, multiple agents, and models should generalize over objects and agents. We propose a novel formalization of relational concurrent activity processes that allows us to transfer methods from standard relational MDPs, such as Monte-Carlo planning and learning from demonstration, to concurrent cooperation domains. We formally compare the formulation to previous propositional models of concurrent decision making and demonstrate planning and learning from demonstration methods on a real-world human-robot assembly task.

关键词： Processes Modeling Concurrent

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Correction: Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

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Journal of neuroengineering and rehabilitation 2024年第1期21卷 71页

作者： M Encarna Micó-Amigo Tecla Bonci Anisoara Paraschiv-Ionescu Martin Ullrich Cameron Kirk Abolfazl Soltani Arne Küderle Eran Gazit Francesca Salis Lisa Alcock Kamiar Aminian Clemens Becker Stefano Bertuletti Philip Brown Ellen Buckley Alma Cantu Anne-Elie Carsin Marco Caruso Brian Caulfield Andrea Cereatti Lorenzo Chiari Ilaria D'Ascanio Bjoern Eskofier Sara Fernstad Marcel Froehlich Judith Garcia-Aymerich Clint Hansen Jeffrey M Hausdorff Hugo Hiden Emily Hume Alison Keogh Felix Kluge Sarah Koch Walter Maetzler Dimitrios Megaritis Arne Mueller Martijn Niessen Luca Palmerini Lars Schwickert Kirsty Scott Basil Sharrack Henrik Sillén David Singleton Beatrix Vereijken Ioannis Vogiatzis Alison J Yarnall Lynn Rochester Claudia Mazzà Silvia Del Din Translational and Clinical Research Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne UK. Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine The University of Sheffield Sheffield UK. Laboratory of Movement Analysis and Measurement Ecole Polytechnique Federale de Lausanne Lausanne Switzerland. Machine Learning and Data Analytics Lab Department of Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen Nürnberg Erlangen Germany. Center for the Study of Movement Cognition and Mobility Neurological Institute Tel Aviv Sourasky Medical Center Tel Aviv Israel. Department of Biomedical Sciences University of Sassari Sassari Italy. Department of Electronics and Telecommunications Politecnico di Torino Turin Italy. National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC) Newcastle University and The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne UK. Robert Bosch Gesellschaft für Medizinische Forschung Stuttgart Germany. The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne UK. School of Computing Newcastle University Newcastle Upon Tyne UK. Barcelona Institute for Global Health (ISGlobal) Barcelona Spain. Universitat Pompeu Fabra Barcelona Catalonia Spain. CIBER Epidemiología y Salud Pública (CIBERESP) Madrid Spain. Insight Centre for Data Analytics University College Dublin Dublin Ireland. School of Public Health Physiotherapy and Sports Science University College Dublin Dublin Ireland. Department of Electrical Electronic and Information Engineering «Guglielmo Marconi» University of Bologna Bologna Italy. Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV) University of Bologna Bologna Italy. Grünenthal GmbH Aachen Germany. Department of Neurology University Medical Center Schleswig-Holstein Campus Kiel Kiel Germany. Sagol School of Neuroscien

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POMDP manipulation via trajectory optimization

POMDP manipulation via trajectory optimization

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015

作者： Vien, Ngo Anh Toussaint, Marc Machine Learning and Robotics Lab University of Stuttgart Germany

ISBN: (纸本)9781479999941

Efficient object manipulation based only on force feedback typically requires a plan of actively contact-seeking actions to reduce uncertainty over the true environmental model. In principle, that problem could be formulated as a full partially observable Markov decision process (POMDP) whose observations are sensed forces indicating the presence/absence of contacts with objects. Such a naive application leads to a very large POMDP with high-dimensional continuous state, action and observation spaces. Solving such large POMDPs is practically prohibitive. In other words, we are facing three challenging problems: 1) uncertainty over discontinuous contacts with objects;2) high-dimensional continuous spaces;3) optimization for not only trajectory cost but also execution time. As trajectory optimization is a powerful model-based method for motion generation, it can handle the last two issues effectively by computing locally optimal trajectories. This paper aims to integrate advantages of trajectory optimization into existing POMDP solvers. The full POMDP formulation is solved using sample-based approaches, where each sampled model is quickly evaluated via trajectory optimization instead of simulating a large number of rollouts. To further accelerate the solver, we propose to integrate temporal abstraction, i.e. macro actions or temporal actions, into the POMDP model. We demonstrate the proposed method on a simulated 7 DoF KUKA arm and a physical Willow Garage PR2 platform. The results show that our proposed method could effectively seek contacts in complex scenarios, and achieve near-optimal performance of path planing. © 2015 IEEE.

关键词： Trajectories

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