Teaching for multi-fingered robots based on motion intention in virtual reality

Kawasaki, Haruhisa; Nakayama, K.; Parker, Graham A.

doi:10.1109/iecon.2000.973188

Cited by 5 publications

(8 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kang et al (14) presented a technique for segmenting the motions involved in human grasps in the real world, but this technique did not consider virtual forces. In our approach (11) , the virtual forces at multiple fingers were used as a key factor in the motion intention analysis. In this case, we assumed that the task handled in motion intention analysis was known.…”

Section: Motion Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

Virtual Robot Teaching Based on Motion Analysis and Hand Manipulability for Multi-Fingered Robot

Kawasaki

Furukawa

Ueki

et al. 2009

JAMDSM

View full text Add to dashboard Cite

A virtual robot teaching system, consisting of human demonstration and motion-intention analysis in a virtual reality environment, is an advanced form of automatic programming for multi-fingered robots. We propose a new segmentation method to analyze human motion data and a virtual teaching system based on hand manipulability, in which the position and orientation of the robot hand are determined so as to maximize the robot hand's manipulability. In the segmentation method, human motion data consisting of contact points, grasp force, hand and object position, and the like are segmented into plural primitive motions and the type of task is analyzed based on the sequence of the primitive motions. A trial assembly task using a humanoid robot hand named Gifu Hand III is shown to demonstrate the effectiveness of the proposed method.

show abstract

Section: Motion Segmentationmentioning

confidence: 99%

“…Our group has presented a concept of virtual robot teaching for multi-fingered robots (11)- (12) , in which the virtual forces at contact points are utilized. Effectiveness of the virtual robot teaching has been shown only for a single task.…”

Section: Introductionmentioning

confidence: 99%

Virtual Robot Teaching Based on Motion Analysis and Hand Manipulability for Multi-Fingered Robot

Kawasaki

Furukawa

Ueki

et al. 2009

JAMDSM

View full text Add to dashboard Cite

show abstract

“…Our method does not employ any specific knowledge about the components of the action sequence. Based on two simple models, the modeling does not require a large set of domain-specific heuristics describing each action primitive as is commonly the case in similar approaches (Pardowitz et al, 2007;Kawasaki et al, 2000;). Due to the simplicity of these two fundamental models and the modeling concepts used within our approach, the developed procedure can be easily used in a wide range of scenarios, like imitation learning, cooperation and assistance.…”

Section: Introductionmentioning

confidence: 99%

“…In general, one is interested in autonomous identification of action primitives in the context of imitation learning and human-machine interaction (Sanmohan, Krüger, & Kragic, 2010;Takano & Nakamura, 2006). Within this domain, Matsuo et al focused on force feedback (Matsuo, Murakami, Hasegawa, Tahara, & Ryo, 2009) while a combination of different sensors like CyberGlove, Vicon or magnetic markers and tactile sensors has been used by (Pardowitz, Knoop, Dillmann, & Zöllner, 2007), (Kawasaki et al, 2000) and (Li, Kulkarni, & Prabhakaran, 2006). In (Zöllner, Asfour, & Dillmann, 2004) a bimanual approach is described.…”

Section: Introductionmentioning

confidence: 99%

“…Analysis of various sensor readings describing the human hand dynamics during manual interaction have been conducted recently by different researchers (Bernardin, Ogawara, Ikeuchi, & Dillmann, 2005;Dillmann, Rogalla, Ehrenmann, Zöllner, & Bordegoni, 2000;Kawasaki, Nakayama, & Parker, 2000). In general, one is interested in autonomous identification of action primitives in the context of imitation learning and human-machine interaction (Sanmohan, Krüger, & Kragic, 2010;Takano & Nakamura, 2006).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hierarchical Bayesian Modeling of Manipulation Sequences from Bimodal Input

Barchunova¹,

Moringen²,

Haschke³

et al. 2012

PsycEXTRA Dataset

View full text Add to dashboard Cite

We propose a hierarchical approach for Bayesian modeling and segmentation of continuous sequences of bimanual object manipulations. Based on bimodal (audio and tactile) low-level time series, the presented approach identifies semantically differing subsequences. It consists of two hierarchically executed stages, each of which employs a Bayesian method for unsupervised change point detection (Fearnhead, 2005). In the first step we propose to use a mixture of model pairs for bimanual tactile data. To this end, we select "object interaction" and "no object interaction" regions for the left and the right hand synchronously. In the second step we apply a set of Autoregressive (AR) models to the audio data. This allows us to select regions within "object interaction" segments according to qualitative changes in the audio signal. Two simple model types that allow the calculation of modality-specific segment likelihoods serve as a foundation for this modeling approach. Based on the acquired ground truth, empirical evaluation has showed that the generated segments correctly capture the semantic structure of the test time series. The developed procedure can serve as a building block for higher-level action and activity modeling frameworks.

show abstract