Daniel Aarno

Publications (16) View all

• Source

  Available from: kth.se

  Conference Proceeding: Layered HMM for Motion Intention Recognition

  D. Aarno, D. Kragic
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  ABSTRACT: Acquiring, representing and modeling human skills is one of the key research areas in teleoperation, programming-by-demonstration and human-machine collaborative settings. One of the common approaches is to divide the task that the operator is executing into several subtask in order to provide manageable modeling. In this paper we consider the use of a layered hidden Markov model (LHMM) to model human skills. We evaluate a gestem classifier that classifies motions into basic action-primitives, or gestems. The gestem classifiers are then used in a LHMM to model a simulated teleoperated task. We investigate the online and offline classification performance with respect to noise, number of gestems, type of HMM and the available number of training sequences. We also apply the LHMM to data recorded during the execution of a trajectory-tracking task in 2D and 3D with a robotic manipulator in order to give qualitative as well as quantitative results for the proposed approach. The results indicate that the LHMM is suitable for modeling teleoperative trajectory-tracking tasks and that the difference in classification performance between one and multi-dimensional HMMs for gestem classification are small. It can also be seen that the LHMM is robust w.r.t misclassifications in the underlying gestem classifiers
  Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on; 11/2006
• Source

  Available from: Florentin Wörgötter

  Chapter: Early Reactive Grasping with Second Order 3D Feature Relations

  Daniel Aarno, Johan Sommerfeld, Danica Kragic, Nicolas Pugeault, Sinan Kalkan, Florentin Wörgötter, Dirk Kraft, Norbert Krüger
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  ABSTRACT: One of the main challenges in the field of robotics is to make robots ubiquitous. To intelligently interact with the world, such robots need to understand the environment and situations around them and react appropriately, they need context-awareness. But how to equip robots with capabilities of gathering and interpreting the necessary information for novel tasks through interaction with the environment and by providing some minimal knowledge in advance? This has been a longterm question and one of the main drives in the field of cognitive system development. The main idea behind the work presented in this paper is that the robot should, like a human infant, learn about objects by interacting with them, forming representations of the objects and their categories that are grounded in its embodiment. For this purpose, we study an early learning of object grasping process where the agent, based on a set of innate reflexes and knowledge about its embodiment. We stress out that this is not the work on grasping, it is a system that interacts with the environment based on relations of 3D visual features generated trough a stereo vision system. We show how geometry, appearance and spatial relations between the features can guide early reactive grasping which can later on be used in a more purposive manner when interacting with the environment.
  03/2007: pages 91-105;
• Source

  Available from: kth.se

  Article: Online task recognition and real-time adaptive assistance for computer-aided machine control

  S. Ekvall, D. Aarno, D. Kragic
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  ABSTRACT: Segmentation and recognition of operator-generated motions are commonly facilitated to provide appropriate assistance during task execution in teleoperative and human-machine collaborative settings. The assistance is usually provided in a virtual fixture framework where the level of compliance can be altered online, thus improving the performance in terms of execution time and overall precision. However, the fixtures are typically inflexible, resulting in a degraded performance in cases of unexpected obstacles or incorrect fixture models. In this paper, we present a method for online task tracking and propose the use of adaptive virtual fixtures that can cope with the above problems. Here, rather than executing a predefined plan, the operator has the ability to avoid unforeseen obstacles and deviate from the model. To allow this, the probability of following a certain trajectory (subtask) is estimated and used to automatically adjusts the compliance, thus providing the online decision of how to fixture the movement
  IEEE Transactions on Robotics 11/2006; · 2.54 Impact Factor
• Chapter: Full-System Simulation from Embedded to High-Performance Systems, Processor and System-on-Chip Simulation

  Jakob Engblom, Daniel Aarno, Bengt Werner
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  ABSTRACT: This chapter describes use cases for and benefits of full-system simulation, based on more than a decade of commercial use of the Simics simulator. Simics has been used to simulate a wide range of systems, from simple single-processor embedded boards to multiprocessor servers and heterogeneous telecom clusters, leading to an emphasis on scalability and flexibility. The most important features and implementation techniques for a high-performance full-system simulator will be described and the techniques to achieve high simulation performance will be discussed in detail. As the ability to efficiently model systems is critical for a full-system simulator, tools and best practices for creating such models will be described. It will be shown how full-system simulation plays a significant role in the development of complex electronic systems, from system definition through development to deployment.
  01/2010: pages 25-45; , ISBN: 978-1-4419-6174-7
• Book: Recognizing Intentions - Improving Human-Machine Collaboration by Recognizing Intentions

  Daniel Aarno
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  ABSTRACT: Robot systems have been used extensively during the last decades to provide automation solutions in a number of areas. Most of the currently deployed automation systems are limited in that the tasks they can solve are required to be repetitive and predicable. Therefore the robotics and artificial intelligence research communities have made significant research efforts to produce more intelligent machines. Although significant progress has been made towards achieving robots that can interact in a human environment there are currently no system that comes close to achieving the reasoning capabilities of humans. In order to reduce the complexity of the problem some researchers have proposed an alternative to creating fully autonomous robots capable of operating in human environments. The proposed alternative is to allow fusion of human and machine capabilities. Segmentation and re­cognition of operator generated motions can be used to provide appropriate assistance during task execution in teleoperative and human-machine collaborative settings. This book describes research towards enabling improved human-machine collaboration using intention recognition.
  11/2008; VDM Verlag Dr. Mueller., ISBN: 978-3639093810