Abstract
In the last decades, cognitive models of multisensory integration in human beings have been developed and applied to model human body experience. Recent research indicates that Bayesian and connectionist models might push developments in various branches of robotics: assistive robotic devices might adapt to their human users aiming at increased device embodiment, e.g., in prosthetics, and humanoid robots could be endowed with human-like capabilities regarding their surrounding space, e.g., by keeping safe or socially appropriate distances to other agents. In this perspective paper, we review cognitive models that aim to approximate the process of human sensorimotor behavior generation, discuss their challenges and potentials in robotics, and give an overview of existing approaches. While model accuracy is still subject to improvement, human-inspired cognitive models support the understanding of how the modulating factors of human body experience are blended. Implementing the resulting insights in adaptive and learning control algorithms could help to taylor assistive devices to their user's individual body experience. Humanoid robots who develop their own body schema could consider this body knowledge in control and learn to optimize their physical interaction with humans and their environment. Cognitive body experience models should be improved in accuracy and online capabilities to achieve these ambitious goals, which would foster human-centered directions in various fields of robotics.
Highlights
The increased interest and progress made toward such capabilities has stimulated research in this direction from which we can draw on a variety of works on robotic self-perception (Sturm et al, 2009; Ulbrich et al, 2009; Lanillos et al, 2017; Lanillos and Cheng, 2018), reviews analyzing connections between human body experience and robotics (Hoffmann et al, 2010; Schillaci et al, 2016; Beckerle et al, 2017) as well as recent works that propose cognitive models of bodily illusions using Bayesian approaches (Samad et al, 2015)
While there are other schools of cognitive modeling (Sun, 2008), we focus on Bayesian approaches due to their relation to human sensorimotor behavior (Körding and Wolpert, 2006; Franklin and Wolpert, 2011) and connectionism because of its relation to developmental psychology (Shultz and Sirois, 2008) and developmental robotics (Lungarella et al, 2003)
Assistive devices might utilize this knowledge by adaptive control improving their integration into their users’ body schemes, i.e., devices could foster their embodiment themselves. We postulate that such models might give humanoid robots a feeling for their own body and its surrounding that can be qualitatively comparable to human body perception, should the situation demand it
Summary
Multisensory integration is a key cognitive function for human body experience (Giummarra et al, 2008; Christ and Reiner, 2014) and cognitive modeling research suggests that it is performed in a Bayesian manner (Deneve and Pouget, 2004; Körding et al, 2007; Orbán and Wolpert, 2011; Clark, 2013). Sun (2008) defines cognitive models as computational models relating to one or multiple cognitive domains or functionalities. The increased interest and progress made toward such capabilities has stimulated research in this direction from which we can draw on a variety of works on robotic self-perception (Sturm et al, 2009; Ulbrich et al, 2009; Lanillos et al, 2017; Lanillos and Cheng, 2018), reviews analyzing connections between human body experience and robotics (Hoffmann et al, 2010; Schillaci et al, 2016; Beckerle et al, 2017) as well as recent works that propose cognitive models of bodily illusions using Bayesian approaches (Samad et al, 2015) Such illusions rely on targeted modulations of multisensory stimulation and make participants perceive artificial limbs as their own (Botvinick and Cohen, 1998; Giummarra et al, 2008; Christ and Reiner, 2014). Cognitive models that go beyond models which described the kinematic structure or dynamic properties of a robot as reviewed in Nguyen-Tuong and Peters (2011), seem to be required
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