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Daniel Aarno

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4 papers
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4

IROS Conference 2006 Conference Paper

Layered HMM for Motion Intention Recognition

  • Daniel Aarno
  • Danica Kragic

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

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ICRA Conference 2005 Conference Paper

Adaptive Virtual Fixtures for Machine-Assisted Teleoperation Tasks

  • Daniel Aarno
  • Staffan Ekvall
  • Danica Kragic

It has been demonstrated in a number of robotic areas how the use of virtual fixtures improves task performance both in terms of execution time and overall precision, [1]. However, the fixtures are typically inflexible, resulting in a degraded performance in cases of unexpected obstacles or incorrect fixture models. In this paper, we propose the use of adaptive virtual fixtures that enable us to cope with the above problems. A teleoperative or human machine collaborative setting is assumed with the core idea of dividing the task, that the operator is executing, into several subtasks. The operator may remain in each of these subtasks as long as necessary and switch freely between them. Hence, rather than executing a predefined plan, the operator has the ability to avoid unforeseen obstacles and deviate from the model. In our system, the probability that the user is following a certain trajectory (subtask) is estimated and used to automatically adjusts the compliance. Thus, an on-line decision of how to fixture the movement is provided.

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ICRA Conference 2005 Conference Paper

Adaptive Virtual Fixtures for Machine-Assisted Teleoperation Tasks

  • Daniel Aarno
  • Staffan Ekvall
  • Danica Kragic

It has been demonstrated in a number of robotic areas how the use of virtual fixtures improves task performance both in terms of execution time and overall precision, [1]. However, the fixtures are typically inflexible, resulting in a degraded performance in cases of unexpected obstacles or incorrect fixture models. In this paper, we propose the use of adaptive virtual fixtures that enable us to cope with the above problems. A teleoperative or human machine collaborative setting is assumed with the core idea of dividing the task, that the operator is executing, into several subtasks. The operator may remain in each of these subtasks as long as necessary and switch freely between them. Hence, rather than executing a predefined plan, the operator has the ability to avoid unforeseen obstacles and deviate from the model. In our system, the probability that the user is following a certain trajectory (subtask) is estimated and used to automatically adjusts the compliance. Thus, an on-line decision of how to fixture the movement is provided.

Details

ICRA Conference 2004 Conference Paper

Artificial Potential Biased Probabilistic Roadmap Method

  • Daniel Aarno
  • Danica Kragic
  • Henrik I. Christensen

Probabilistic roadmap methods (PRM) have been successfully used to solve difficult path planning problems but their efficiency is limited when the free space contains narrow passages through which the robot must pass. This paper presents a new sampling scheme that aims to increase the probability of finding paths through narrow passages. Here, a biased sampling scheme is used to increase the distribution of nodes in narrow regions of the free space. A partial computation of the artificial potential field is used to bias the distribution of nodes.

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