Visually Guided Whole Body Interaction

Bolder, Bram; Dunn, Mark; Gienger, Michael; Janßen, H.; Sugiura, Hisashi; Goerick, Christian

doi:10.1109/robot.2007.363936

Cited by 24 publications

(15 citation statements)

References 26 publications

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“…Specifically, the sheet was oriented to be flat (horizontal), tilted ±16 • and ±27 • about the x-axis (horizontal axis pointing directly ahead from the robot) and ±16 • about the y-axis (horizontal right-left axis). The three-dimensional coordinates of the sponge were tracked using the on-board stereo cameras of the ASIMO robot at a rate of 20 frames per second (for details on the ASIMO vision system please see Bolder et al 2007). The recorded trajectories are shown in Fig.…”

Section: Washing a Carmentioning

confidence: 99%

A novel method for learning policies from variable constraint data

et al. 2009

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Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. Constraints are usually unobservable and frequently change between contexts. In this paper, we present a novel approach for learning (unconstrained) control policies from movement data, where observations come from movements under different constraints. As a key ingredient, we introduce a small but highly effective modification to the standard risk functional, allowing us to make a meaningful comparison between the estimated policy and constrained observations. We demonstrate our approach on systems of varying complexity, including kinematic data from the ASIMO humanoid robot with 27 degrees of freedom, and present results for learning from human demonstration.

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Section: Washing a Carmentioning

confidence: 99%

A novel method for learning policies from variable constraint data

et al. 2009

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“…When using our humanoid robot ASIMO instead, its degrees of freedom are used to track but also follow the current peri-personal hypothesis [13]. The proposed system runs in real-time with a frame-rate of 6Hz.…”

Section: Systemmentioning

confidence: 99%

Large-Scale Real-Time Object Identification Based on Analytic Features

Hasler

Wersing

Kirstein

et al. 2009

Artificial Neural Networks – ICANN 2009

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Abstract. Inspired by biological findings, we present a system that is able to robustly identify a large number of pre-trained objects in realtime. In contrast to related work, we do not restrict the objects' pose to characteristic views but rotate them freely in hand in front of a cluttered background. We describe the essential system's ingredients, like prototype-based figure-ground segmentation, extraction of brain-like analytic features, and a simple classifier on top. Finally we analyze the performance of the system using databases of varying difficulty.

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“…, and G 1 , G 2 and G p (j) are the gains calculated from the weights Q and R and the parameters used in the system described by equation (2). It is interesting to note the unicity of the weights G p (j) for a given control period T , preview window N L and constant CoM height z c .…”

Section: Com Trajectory Generationmentioning

confidence: 99%

Integrating Walking and Vision to Increase Humanoid Autonomy

Stasse¹,

Saidi²,

Yokoi³

et al. 2008

Int. J. Human. Robot.

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Aiming at building versatile humanoid systems, we present in this paper the real-time implementation of behaviors which integrate walking and vision to achieve general functionalities. This paper describes how real-time -or high bandwidth-cognitive processes can be obtained by combining vision with walking. The central point of our methodology is to use appropriate models to reduce the complexity of the search space. We will describe the models introduced in the different blocks of the system and their relationships: walking pattern, Self Localization and Map Building, real-time reactive vision behaviors, and planning.

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Visually Guided Whole Body Interaction

Cited by 24 publications

References 26 publications

A novel method for learning policies from variable constraint data

A novel method for learning policies from variable constraint data

Large-Scale Real-Time Object Identification Based on Analytic Features

Integrating Walking and Vision to Increase Humanoid Autonomy

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