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IEEE/RSJ International Conference on intelligent Robots and Systems

作者： Zhengkun Yi Roberto Calandra Filipe Veiga Herke van Hoof Tucker Hermans Yilei Zhang Jan Peters Intelligent Autonomous Systems Lab Department of Computer Science Technische Universitat Darmstadt Hochschulstr. 10 64289 Germany School of Mechanical and Aerospace Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 University of Utah Robotics Center School of Computing University of Utah USA

ISBN: (纸本)9781509037636

Accurate object shape knowledge provides important information for performing stable grasping and dexterous manipulation. When modeling an object using tactile sensors, touching the object surface at a fixed grid of points can be sample inefficient. In this paper, we present an active touch strategy to efficiently reduce the surface geometry uncertainty by leveraging a probabilistic representation of object surface. In particular, we model the object surface using a Gaussian process and use the associated uncertainty information to efficiently determine the next point to explore. We validate the resulting method for tactile object surface modeling using a real robot to reconstruct multiple, complex object surfaces.

关键词： Gaussian processes Touch tactile sensors grid point

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Learning multiple collaborative tasks with a mixture of Interaction Primitives

Learning multiple collaborative tasks with a mixture of Inte...

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2015 IEEE International Conference on robotics and Automation, ICRA 2015

作者： Ewerton, Marco Neumann, Gerhard Lioutikov, Rudolf Amor, Heni Ben Peters, Jan Maeda, Guilherme Intelligent Autonomous Systems Lab Department of Computer Science Technische Universität Darmstadt Hochschulstr. 10 Darmstadt64289 Germany Institute of Robotics and Intelligent Machines Georgia Institute of Technology 801 Atlantic Drive AtlantaGA30332-0280 United States Max Planck Institute for Intelligent Systems Spemannstr. 38 Tuebingen72076 Germany

ISBN: (纸本)9781479969234

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. © 2015 IEEE.

关键词： Demonstrations

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A skill-based system for object perception and manipulation for automating kitting tasks

A skill-based system for object perception and manipulation ...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Dirk Holz Angeliki Topalidou-Kyniazopoulou Francesco Rovida Mikkel Rath Pedersen Volker Krüger Sven Behnke Autonomous Intelligent Systems Group University of Bonn Bonn Germany Robotics Vision and Machine Intelligence (RVMI) Lab Aalborg University Copenhagen Denmark

The automation of kitting tasks-collecting a set of parts for one particular car into a kit-has a huge impact in the automotive industry. It considerably increases the automation levels of tasks typically conducted by human workers. Collecting the parts involves picking up objects from pallets and bins as well as placing them in the respective compartments of the kitting box. In this paper, we present a complete system for automated kitting with a mobile manipulator thereby focusing on depalletizing tasks and placing. In order to allow for low cycle times, we present particularly efficient solutions to object perception as well as motion planning and execution. For easy portability to different platforms, all components are integrated into a skill-based framework that is tightly coupled with a task planning component. We present results of experiments at both a research laboratory environment and at the industrial site of PSA Peugeot Citroën serving as a proof of concept for the overall system design and implementation.

关键词： Robot kinematics Planning Software Service robots Mobile communication Three-dimensional displays

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Understanding the geometry of workspace obstacles in Motion Optimization

Understanding the geometry of workspace obstacles in Motion ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Nathan Ratliff Marc Toussaint Stefan Schaal Autonomous Motion Department Max Planck Institute for Intelligent Systems Tübingen Germany Machine Learning and Robotics Lab University of Stuttgart Germany

ISBN: (纸本)9781479969241

What is it that makes movement around obstacles hard? The answer seems clear: obstacles contort the geometry of the workspace and make it difficult to leverage what we consider easy and intuitive straight-line Cartesian geometry. But is Cartesian motion actually easy? It's certainly well-understood and has numerous applications. But beneath the details of linear algebra and pseudoinverses, lies a non-trivial Riemannian metric driving the solution. Cartesian motion is easy only because the pseudoinverse, our powerhouse tool, correctly represents how Euclidean workspace geometry pulls back into the configuration space. In light of that observation, it reasons that motion through a field of obstacles could be just as easy as long as we correctly account for how those obstacles warp the geometry of the space. This paper explores extending our geometric model of the robot beyond the notion of a Cartesian workspace space to fully model and leverage how geometry changes in the presence of obstacles. Intuitively, impenetrable obstacles form topological holes and geodesics curve around them accordingly. We formalize this intuition and develop a general motion optimization framework called Riemannian Motion Optimization (RieMO) to efficiently find motions using our geometric models. Our experiments demonstrate that, for many problems, obstacle avoidance can be much more natural when placed within the right geometric context.

关键词： Geometry Optimization Measurement Approximation methods Trajectory Robots Kinematics

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Where's waldo at time t ? using spatio-temporal models for mobile robot search

Where's waldo at time t ? using spatio-temporal models for m...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Tomáš Krajník Miroslav Kulich Lenka Mudrová Rares Ambrus Tom Duckett Lincoln Centre for Autonomous Systems University of Lincoln Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Intelligent Robotics Lab University of Birmingham UK KTH Royal Institute of Technology Sweden

ISBN: (纸本)9781479969241

We present a novel approach to mobile robot search for non-stationary objects in partially known environments. We formulate the search as a path planning problem in an environment where the probability of object occurrences at particular locations is a function of time. We propose to explicitly model the dynamics of the object occurrences by their frequency spectra. Using this spectral model, our path planning algorithm can construct plans that reflect the likelihoods of object locations at the time the search is performed. Three datasets collected over several months containing person and object occurrences in residential and office environments were chosen to evaluate the approach. Several types of spatio-temporal models were created for each of these datasets and the efficiency of the search method was assessed by measuring the time it took to locate a particular object. The results indicate that modeling the dynamics of object occurrences reduces the search time by 25% to 65% compared to maps that neglect these dynamics.

关键词： Search problems Mobile robots Testing Robot sensing systems Training

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Inertial parameters identification and joint torques estimation with proximal force/torque sensing

Inertial parameters identification and joint torques estimat...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Silvio Traversaro Andrea Del Prete Serena Ivaldi Francesco Nori Robotics Brain and Cognitive Sciences Department Istituto Italiano di Tecnologia Genoa Italy LAAS/CNRS Toulouse France Inria Villers-lès-Nancy France Intelligent Autonomous Systems Lab. TU Darmstadt Germany CNRS Loria UMR n.7503 and Université de Lorraine Loria France

ISBN: (纸本)9781479969241

Classically robot force control passes through joint torques measurement or estimation. Within this context, classical torque sensing technologies rely on current sensing on motor windings and on torsion sensing on motor shaft. An alternative approach was recently proposed in [1] and combines whole-body distributed 6-axis force/torque (F/T) sensors, gyroscopes, accelerometers and tactile sensors (i.e. artificial skin). A further advantage of this method is that it simultaneously estimates (internal) joint torques and (external) contact forces with no need of joint redesign. As a drawback, the method relies on a model of the robot dynamics, as it consists on reordering the classical recursive Newton-Euler algorithm (RNEA). In this paper we consider the problem of the parametric identification of the robot dynamic model from embedded F/T sensors. We extend recent results on parametric identification [2] by considering an arbitrary reordering of the classical RNEA. The theoretical framework is validated on the iCub humanoid, which is equipped with both 6-axis F/T sensors and joint torque sensors. We estimated the system inertial parameters using only one F/T sensor. We used the obtained parameters to estimate the joint torques (as proposed in [1]) and compared the results with direct joint torque measurements, used in this context only as a ground truth.

关键词： Joints Force Torque Estimation Robot sensing systems

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Tools for simulating humanoid robot dynamics: A survey based on user feedback

Tools for simulating humanoid robot dynamics: A survey based...

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IEEE-RAS International Conference on Humanoid Robots

作者： Serena Ivaldi Jan Peters Vincent Padois Francesco Nori TU Darmstadt The Intelligent Autonomous Systems Lab Germany The Max Planck Institute for Intelligent Systems CNRS Sorbonne Universités France The Robotics Italian Institute of Technology

ISBN: (纸本)9781479971756

The number of tools for dynamics simulation has grown substantially in the last few years. Humanoid robots, in particular, make extensive use of such tools for a variety of applications, from simulating contacts to planning complex motions. It is necessary for the humanoid robotics community to have a systematic evaluation to assist in choosing which of the available tools is best for their research. This paper surveys the state of the art in dynamics simulation and reports on the analysis of an online survey about the use of dynamics simulation in the robotics research community. The major requirements for robotics researchers are better physics engines and open-source software. Despite the numerous tools, there is not a general-purpose simulator which dominates the others in terms of performance or application. However, for humanoid robotics, Gazebo emerges as the best choice among the open-source projects, while V-Rep is the preferred commercial simulator. The survey report has been instrumental for choosing Gazebo as the base for the new simulator for the iCub humanoid robot.

关键词： Robots Physics Engines Open source software Communities Collision avoidance

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Learning interaction for collaborative tasks with probabilistic movement primitives

Learning interaction for collaborative tasks with probabilis...

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IEEE-RAS International Conference on Humanoid Robots

作者： Guilherme Maeda Marco Ewerton Rudolf Lioutikov Heni Ben Amor Jan Peters Gerhard Neumann Intelligent Autonomous Systems Lab Technische Universitaet Darm-stadt Darmstadt Germany Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta GA USA Max Planck Institute for Intelligent Systems Tuebingen Germany

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.

关键词： Trajectory Robot kinematics Hidden Markov models Collaboration Vectors Probabilistic logic

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Combining Top-down Spatial Reasoning and Bottom-up Object Class Recognition for Scene Understanding

Combining Top-down Spatial Reasoning and Bottom-up Object Cl...

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IEEE/RSJ International Conference on intelligent Robots and Systems

作者： Lars Kunze Chris Burbridge Marina Alberti Akshaya Tippur John Folkesson Patric Jensfelt Nick Hawes Intelligent Robotics Lab School of Computer Science University of Birmingham United Kingdom Centre for Autonomous Systems KTH Royal Institute of Technology SE-100 44 Stockholm Sweden

ISBN: (纸本)9781479969357

Many robot perception systems are built to only consider intrinsic object features to recognise the class of an object. By integrating both top-down spatial relational reasoning and bottom-up object class recognition the overall performance of a perception system can be improved. In this paper we present a unified framework that combines a 3D object class recognition system with learned, spatial models of object relations. In robot experiments we show that our combined approach improves the classification results on real world office desks compared to pure bottom-up perception. Hence, by using spatial knowledge during object class recognition perception becomes more efficient and robust and robots can understand scenes more effectively.

关键词： object class Object Relations Robot bottom-up spatial reasoning senses Perception top-down

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Simulation and HRI recent perspectives with the MORSE simulator

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8810卷 13-24页

作者： Lemaignan, Séverin Hanheide, Marc Karg, Michael Khambhaita, Harmish Kunze, Lars Lier, Florian Lütkebohle, Ingo Milliez, Grégoire CHILI Lab. EPFL Lausanne Switzerland Centre for Autonomous Systems University of Lincoln United Kingdom IAS Technische Universität München Germany LAAS/CNRS Université de Toulouse France Intelligent Robotics Lab. University of Birmingham United Kingdom CITEC Bielefeld University Germany Machine Learning and Robotics Lab. Universität Stuttgart Germany

Simulation in robotics is often a love-hate relationship: while simulators do save us a lot of time and effort compared to regular deployment of complex software architectures on complex hardware, simulators are also known to evade many of the real issues that robots need to manage when they enter the real world. Because humans are the paragon of dynamic, unpredictable, complex, real world entities, simulation of human-robot interactions may look condemn to fail, or, in the best case, to be mostly useless. This collective article reports on five independent applications of the MORSE simulator in the field of human-robot interaction: It appears that simulation is already useful, if not essential, to successfully carry out research in the field of HRI, and sometimes in scenarios we do not anticipate. © 2014 Springer International Publishing Switzerland.

关键词： Simulators

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