Autonomous failure detection and multimodal sensor fusion in a modular arm model

Ehrenfeld, Stephan; Butz, Martin V.

doi:10.1109/iros.2012.6385670

Cited by 1 publication

(7 citation statements)

References 15 publications

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“…As Gl 2 's receptive field (Equation 29) is narrower than all other receptive fields, its dispersion is lower, and m GL 2 mainly detects the different dispersions, while it might be more interesting to instead detect systematic errors of the mean. Thus, for this application, a dispersion-independent measure (Ehrenfeld and Butz, 2012, 2013) might be more appropriate. This would yield much higher measures m GL 2 than shown in Figure 7, top-right.…”

Section: Resultsmentioning

confidence: 99%

“…This improvement is even visible under strong noise (red vs. yellow in Figure 8). Again, a dispersion-independent measure (Ehrenfeld and Butz, 2012, 2013) could improve the performance.…”

Section: Resultsmentioning

confidence: 99%

“…The same four modules and respective coordinate systems exist for the forearm and the upper arm (not shown). Modified based on Ehrenfeld and Butz (2012, 2013). …”

Section: Methodsmentioning

confidence: 99%

“…Yellow dash-dotted lines are the forward kinematics, dark gray dotted lines the inverse kinematics, and red solid lines the distal-to-proximal kinematics. Modified based on Ehrenfeld and Butz (2012, 2013). …”

Section: Methodsmentioning

confidence: 99%

“…We recently proposed the Modular Modality Frame (MMF) model (Ehrenfeld and Butz, 2011, 2012, 2013), which models the maintenance of a body state estimate given noisy, multimodal sensory information sources. The MMF model fully relies on hard-coded kinematic knowledge of the simulated body and estimates body states by means of Gaussian probability densities.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Modular neuron-based body estimation: maintaining consistency over different limbs, modalities, and frames of reference

Ehrenfeld

Herbort

Butz

2013

Front. Comput. Neurosci.

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This paper addresses the question of how the brain maintains a probabilistic body state estimate over time from a modeling perspective. The neural Modular Modality Frame (nMMF) model simulates such a body state estimation process by continuously integrating redundant, multimodal body state information sources. The body state estimate itself is distributed over separate, but bidirectionally interacting modules. nMMF compares the incoming sensory and present body state information across the interacting modules and fuses the information sources accordingly. At the same time, nMMF enforces body state estimation consistency across the modules. nMMF is able to detect conflicting sensory information and to consequently decrease the influence of implausible sensor sources on the fly. In contrast to the previously published Modular Modality Frame (MMF) model, nMMF offers a biologically plausible neural implementation based on distributed, probabilistic population codes. Besides its neural plausibility, the neural encoding has the advantage of enabling (a) additional probabilistic information flow across the separate body state estimation modules and (b) the representation of arbitrary probability distributions of a body state. The results show that the neural estimates can detect and decrease the impact of false sensory information, can propagate conflicting information across modules, and can improve overall estimation accuracy due to additional module interactions. Even bodily illusions, such as the rubber hand illusion, can be simulated with nMMF. We conclude with an outlook on the potential of modeling human data and of invoking goal-directed behavioral control.

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Section: Resultsmentioning

confidence: 99%

“…This improvement is even visible under strong noise (red vs. yellow in Figure 8). Again, a dispersion-independent measure (Ehrenfeld and Butz, 2012, 2013) could improve the performance.…”

Section: Resultsmentioning

confidence: 99%

“…The same four modules and respective coordinate systems exist for the forearm and the upper arm (not shown). Modified based on Ehrenfeld and Butz (2012, 2013). …”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%