How Cognitive Models of Human Body Experience Might Push Robotics

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.

Effect of External Force on Agency in Physical Human-Machine Interaction

“…Another issue in using SoA scores for evaluating and differentiating more complex controllers is the fact that we have to delay the visual feedback in our paradigm. For a control design aiming at optimizing user experience, we would need to estimate SoA ambiently so the controller can adapt online, e.g., using cognitive models (Schürmann et al, 2019). For instance, controlling the attitude of the robot toward disagreements in early phases of the interaction is considered as an important issue for HMI (Hancock et al, 2011).…”

Benchmarking Wearable Robots: Challenges and Recommendations From Functional, User Experience, and Methodological Perspectives

“…These might include psychometric tools to evaluate subjective experience (Hart and Staveland, 1988 ; Longo et al, 2008 ; Caspar et al, 2015 ) as well as more objective behavioral measures, e.g., proprioceptive drift for embodiment (Christ and Reiner, 2014 ), intentional binding techniques for agency (Caspar et al, 2015 ; Endo et al, 2020 ), or physiological measures, such as heart rate (Ikehara and Crosby, 2005 ), electrodermal activity, or neurophysiological measures (Christ and Reiner, 2014 ). Such systematic measures might not only be used to consider user experience in WR design, but could also be a means to implement adaptive control schemes that coordinate control behavior to improve user experience, e.g., predicting embodiment outcome to foster it by appropriately adjusted control (Schürmann et al, 2019 ). While physiological measurements and electrical stimulation might support this by exploiting neuroplastic effects, deeper investigation of brain plasticity is subject to ongoing research (McGie et al, 2015 ; Makin et al, 2017 ).…”

Personalizing Human-Agent Interaction Through Cognitive Models

“…Here, robotic agents may use cognitive models to predict human interactions, but they may also control their own sensorimotor behavior by use of such a cognitive model. The benefit of applying cognitive approaches lies in the potentially realistic imitation of human behavior and can foster both psychological research and the development of humanoid robots (Asada et al, 2009;Hoffmann et al, 2010;Schillaci et al, 2016;Prescott et al, 2019;Schürmann et al, 2019). Through fitting free parameters to interindividual differences, behavior prediction and generation can be personalized rather straightforwardly.…”

“…Therefore, Lee (2011) highlights the importance of accounting for individual differences in the execution of cognitive functions and proposes hierarchical cognitive modeling as a way to do so. Using cognitive models to estimate and then maintain a representation of the motivational, emotional, or other cognitive states of individuals allows an interactive system to adjust its behavior and, accordingly, may help to personalize user experience with HAI systems ( Schürmann et al, 2019 ). It is still debated whether statistical or verbal-conceptual models ( Sun, 2008a ; Çelikok et al, 2019 ; Guest and Martin, 2020 ) provide the required conceptual precision to shed light on the underlying theory.…”