A modular, redundant, multi-frame of reference representation for kinematic chains

Abstract

When dealing with light-weight robots with nonrigid limbs and joints and uncertain sensory readings, configuration state representations inevitably are approximate. Due to various types of sensory readings, which are usually body-grounded in different frames of reference, these configuration states may naturally be represented modularly distributed. From a different perspective, computational models of human motor planning suggest that the brain represents current body postures, such as the state of an arm, modularly within various frames of reference. Moreover, the different information sources, such as proprioceptive, anticipatory, and visual information, are probabilistically integrated. As a basis for such a representation, we propose a modular system that maintains a distributed self-representation in interactive frames of reference. We show that the resulting representation is highly noise robust and may be used to reach goals within various frames of reference while also considering other task constraints.

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

“…As in the previously published Gaussian MMF model (Ehrenfeld and Butz, 2011, 2012, 2013), nMMF represents the body (an arm in the current implementation) modularized into body parts and sensor-respective frames of reference. Local, body-state-dependent mappings allow for continuous interactions between modules, ensuring consistency.…”

“…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.…”