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Sandini G, Sciutti A, Morasso P. Artificial cognition vs. artificial intelligence for next-generation autonomous robotic agents. Front Comput Neurosci 2024; 18:1349408. [PMID: 38585280 PMCID: PMC10995397 DOI: 10.3389/fncom.2024.1349408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 02/20/2024] [Indexed: 04/09/2024] Open
Abstract
The trend in industrial/service robotics is to develop robots that can cooperate with people, interacting with them in an autonomous, safe and purposive way. These are the fundamental elements characterizing the fourth and the fifth industrial revolutions (4IR, 5IR): the crucial innovation is the adoption of intelligent technologies that can allow the development of cyber-physical systems, similar if not superior to humans. The common wisdom is that intelligence might be provided by AI (Artificial Intelligence), a claim that is supported more by media coverage and commercial interests than by solid scientific evidence. AI is currently conceived in a quite broad sense, encompassing LLMs and a lot of other things, without any unifying principle, but self-motivating for the success in various areas. The current view of AI robotics mostly follows a purely disembodied approach that is consistent with the old-fashioned, Cartesian mind-body dualism, reflected in the software-hardware distinction inherent to the von Neumann computing architecture. The working hypothesis of this position paper is that the road to the next generation of autonomous robotic agents with cognitive capabilities requires a fully brain-inspired, embodied cognitive approach that avoids the trap of mind-body dualism and aims at the full integration of Bodyware and Cogniware. We name this approach Artificial Cognition (ACo) and ground it in Cognitive Neuroscience. It is specifically focused on proactive knowledge acquisition based on bidirectional human-robot interaction: the practical advantage is to enhance generalization and explainability. Moreover, we believe that a brain-inspired network of interactions is necessary for allowing humans to cooperate with artificial cognitive agents, building a growing level of personal trust and reciprocal accountability: this is clearly missing, although actively sought, in current AI. The ACo approach is a work in progress that can take advantage of a number of research threads, some of them antecedent the early attempts to define AI concepts and methods. In the rest of the paper we will consider some of the building blocks that need to be re-visited in a unitary framework: the principles of developmental robotics, the methods of action representation with prospection capabilities, and the crucial role of social interaction.
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Affiliation(s)
| | | | - Pietro Morasso
- Italian Institute of Technology, Cognitive Architecture for Collaborative Technologies (CONTACT) and Robotics, Brain and Cognitive Sciences (RBCS) Research Units, Genoa, Italy
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2
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Explaining Aha! moments in artificial agents through IKE-XAI: Implicit Knowledge Extraction for eXplainable AI. Neural Netw 2022; 155:95-118. [DOI: 10.1016/j.neunet.2022.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 07/21/2022] [Accepted: 08/01/2022] [Indexed: 11/20/2022]
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3
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Hafner V, Hommel B, Kayhan E, Lee D, Paulus M, Verschoor S. Editorial: The Mechanisms Underlying the Human Minimal Self. Front Psychol 2022; 13:961480. [PMID: 35846650 PMCID: PMC9285898 DOI: 10.3389/fpsyg.2022.961480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 06/21/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Verena Hafner
- Adaptive Systems Group, Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bernhard Hommel
- Department of Psychology, Shandong Normal University, Jinan, China
- University Hospital Carl Gustav Carus, Dresden, Germany
- *Correspondence: Bernhard Hommel
| | - Ezgi Kayhan
- Department of Developmental Psychology, University of Potsdam, Potsdam, Germany
| | - Dongheui Lee
- Human-Centered Assistive Robotics, Technical University of Munich, Munich, Germany
| | - Markus Paulus
- Developmental Psychology, Ludwig Maximilians-Universität München, Munich, Germany
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4
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Valenzo D, Ciria A, Schillaci G, Lara B. Grounding Context in Embodied Cognitive Robotics. Front Neurorobot 2022; 16:843108. [PMID: 35812785 PMCID: PMC9262126 DOI: 10.3389/fnbot.2022.843108] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
Biological agents are context-dependent systems that exhibit behavioral flexibility. The internal and external information agents process, their actions, and emotions are all grounded in the context within which they are situated. However, in the field of cognitive robotics, the concept of context is far from being clear with most studies making little to no reference to it. The aim of this paper is to provide an interpretation of the notion of context and its core elements based on different studies in natural agents, and how these core contextual elements have been modeled in cognitive robotics, to introduce a new hypothesis about the interactions between these contextual elements. Here, global context is categorized as agent-related, environmental, and task-related context. The interaction of their core elements, allows agents to first select self-relevant tasks depending on their current needs, or for learning and mastering their environment through exploration. Second, to perform a task and continuously monitor its performance. Third, to abandon a task in case its execution is not going as expected. Here, the monitoring of prediction error, the difference between sensorimotor predictions and incoming sensory information, is at the core of behavioral flexibility during situated action cycles. Additionally, monitoring prediction error dynamics and its comparison with the expected reduction rate should indicate the agent its overall performance on executing the task. Sensitivity to performance evokes emotions that function as the driving element for autonomous behavior which, at the same time, depends on the processing of the interacting core elements. Taking all these into account, an interactionist model of contexts and their core elements is proposed. The model is embodied, affective, and situated, by means of the processing of the agent-related and environmental core contextual elements. Additionally, it is grounded in the processing of the task-related context and the associated situated action cycles during task execution. Finally, the model proposed here aims to guide how artificial agents should process the core contextual elements of the agent-related and environmental context to give rise to the task-related context, allowing agents to autonomously select a task, its planning, execution, and monitoring for behavioral flexibility.
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Affiliation(s)
- Diana Valenzo
- Laboratorio de Robótica Cognitiva, Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca, Mexico
| | - Alejandra Ciria
- Facultad de Psicología, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | | | - Bruno Lara
- Laboratorio de Robótica Cognitiva, Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca, Mexico
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5
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He D, Ogmen H. Sensorimotor Self-organization via Circular-Reactions. Front Neurorobot 2021; 15:658450. [PMID: 34966265 PMCID: PMC8710445 DOI: 10.3389/fnbot.2021.658450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 11/22/2021] [Indexed: 11/15/2022] Open
Abstract
Newborns demonstrate innate abilities in coordinating their sensory and motor systems through reflexes. One notable characteristic is circular reactions consisting of self-generated motor actions that lead to correlated sensory and motor activities. This paper describes a model for goal-directed reaching based on circular reactions and exocentric reference-frames. The model is built using physiologically plausible visual processing modules and arm-control neural networks. The model incorporates map representations with ego- and exo-centric reference frames for sensory inputs, vector representations for motor systems, as well as local associative learning that result from arm explorations. The integration of these modules is simulated and tested in a three-dimensional spatial environment using Unity3D. The results show that, through self-generated activities, the model self-organizes to generate accurate arm movements that are tolerant with respect to various sources of noise.
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Affiliation(s)
- Dongcheng He
- Laboratory of Perceptual and Cognitive Dynamics, Department of Electrical & Computer Engineering, Ritchie School of Engineering and Computer Science, University of Denver, Denver, CO, United States
| | - Haluk Ogmen
- Laboratory of Perceptual and Cognitive Dynamics, Department of Electrical & Computer Engineering, Ritchie School of Engineering and Computer Science, University of Denver, Denver, CO, United States
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6
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Röder F, Özdemir O, Nguyen PDH, Wermter S, Eppe M. The Embodied Crossmodal Self Forms Language and Interaction: A Computational Cognitive Review. Front Psychol 2021; 12:716671. [PMID: 34484079 PMCID: PMC8415221 DOI: 10.3389/fpsyg.2021.716671] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 07/16/2021] [Indexed: 11/13/2022] Open
Abstract
Human language is inherently embodied and grounded in sensorimotor representations of the self and the world around it. This suggests that the body schema and ideomotor action-effect associations play an important role in language understanding, language generation, and verbal/physical interaction with others. There are computational models that focus purely on non-verbal interaction between humans and robots, and there are computational models for dialog systems that focus only on verbal interaction. However, there is a lack of research that integrates these approaches. We hypothesize that the development of computational models of the self is very appropriate for considering joint verbal and physical interaction. Therefore, they provide the substantial potential to foster the psychological and cognitive understanding of language grounding, and they have significant potential to improve human-robot interaction methods and applications. This review is a first step toward developing models of the self that integrate verbal and non-verbal communication. To this end, we first analyze the relevant findings and mechanisms for language grounding in the psychological and cognitive literature on ideomotor theory. Second, we identify the existing computational methods that implement physical decision-making and verbal interaction. As a result, we outline how the current computational methods can be used to create advanced computational interaction models that integrate language grounding with body schemas and self-representations.
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Affiliation(s)
- Frank Röder
- Knowledge Technology, Department of Informatics, University of Hamburg, Hamburg, Germany
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7
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Ciria A, Schillaci G, Pezzulo G, Hafner VV, Lara B. Predictive Processing in Cognitive Robotics: A Review. Neural Comput 2021; 33:1402-1432. [PMID: 34496394 DOI: 10.1162/neco_a_01383] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 12/31/2020] [Indexed: 11/04/2022]
Abstract
Predictive processing has become an influential framework in cognitive sciences. This framework turns the traditional view of perception upside down, claiming that the main flow of information processing is realized in a top-down, hierarchical manner. Furthermore, it aims at unifying perception, cognition, and action as a single inferential process. However, in the related literature, the predictive processing framework and its associated schemes, such as predictive coding, active inference, perceptual inference, and free-energy principle, tend to be used interchangeably. In the field of cognitive robotics, there is no clear-cut distinction on which schemes have been implemented and under which assumptions. In this letter, working definitions are set with the main aim of analyzing the state of the art in cognitive robotics research working under the predictive processing framework as well as some related nonrobotic models. The analysis suggests that, first, research in both cognitive robotics implementations and nonrobotic models needs to be extended to the study of how multiple exteroceptive modalities can be integrated into prediction error minimization schemes. Second, a relevant distinction found here is that cognitive robotics implementations tend to emphasize the learning of a generative model, while in nonrobotics models, it is almost absent. Third, despite the relevance for active inference, few cognitive robotics implementations examine the issues around control and whether it should result from the substitution of inverse models with proprioceptive predictions. Finally, limited attention has been placed on precision weighting and the tracking of prediction error dynamics. These mechanisms should help to explore more complex behaviors and tasks in cognitive robotics research under the predictive processing framework.
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Affiliation(s)
- Alejandra Ciria
- Facultad de Psicología, Universidad Nacional Autónoma de México, Mexico City, CP 04510, Mexico
| | - Guido Schillaci
- BioRobotics Institute, Scuola Superiore Sant'Anna, 34 56025 Pontedera, Italy
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, 44 00185 Rome, Italy
| | - Verena V Hafner
- Adaptive Systems Group, Department of Computer Science, Humboldt-Universität zu Berlin, D-12489, Germany
| | - Bruno Lara
- Laboratorio de Robótica Cognitiva, Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca CP 62209, Mexico
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8
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Schürmann T, Beckerle P. Personalizing Human-Agent Interaction Through Cognitive Models. Front Psychol 2020; 11:561510. [PMID: 33071887 PMCID: PMC7541964 DOI: 10.3389/fpsyg.2020.561510] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 08/14/2020] [Indexed: 11/13/2022] Open
Abstract
Cognitive modeling of human behavior has advanced the understanding of underlying processes in several domains of psychology and cognitive science. In this article, we outline how we expect cognitive modeling to improve comprehension of individual cognitive processes in human-agent interaction and, particularly, human-robot interaction (HRI). We argue that cognitive models offer advantages compared to data-analytical models, specifically for research questions with expressed interest in theories of cognitive functions. However, the implementation of cognitive models is arguably more complex than common statistical procedures. Additionally, cognitive modeling paradigms typically have an explicit commitment to an underlying computational theory. We propose a conceptual framework for designing cognitive models that aims to identify whether the use of cognitive modeling is applicable to a given research question. The framework consists of five external and internal aspects related to the modeling process: research question, level of analysis, modeling paradigms, computational properties, and iterative model development. In addition to deriving our framework from a concise literature analysis, we discuss challenges and potentials of cognitive modeling. We expect cognitive models to leverage personalized human behavior prediction, agent behavior generation, and interaction pretraining as well as adaptation, which we outline with application examples from personalized HRI.
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Affiliation(s)
- Tim Schürmann
- Work and Engineering Psychology Research Group, Department of Human Sciences, Technical University of Darmstadt, Darmstadt, Germany
| | - Philipp Beckerle
- Elastic Lightweight Robotics, Department of Electrical Engineering and Information Technology, Robotics Research Institute, Technische Universität Dortmund, Dortmund, Germany.,Institute for Mechatronic Systems, Mechanical Engineering, Technical University of Darmstadt, Darmstadt, Germany
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9
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Hoffmann M, Lanillos P, Jamone L, Pitti A, Somogyi E. Editorial: Body Representations, Peripersonal Space, and the Self: Humans, Animals, Robots. Front Neurorobot 2020; 14:35. [PMID: 32612519 PMCID: PMC7308760 DOI: 10.3389/fnbot.2020.00035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 05/14/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Matej Hoffmann
- Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University, Prague, Czechia
| | - Pablo Lanillos
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Lorenzo Jamone
- ARQ (Advanced Robotics at Queen Mary), School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
| | - Alex Pitti
- Laboratoire ETIS, CY Cergy Paris University, ENSEA, CNRS, UMR8051, Cergy-Pontoise, France
| | - Eszter Somogyi
- Department of Psychology, Centre for Situated Action & Communication, University of Portsmouth, Portsmouth, United Kingdom
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10
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Acevedo-Valle JM, Hafner VV, Angulo C. Social Reinforcement in Artificial Prelinguistic Development: A Study Using Intrinsically Motivated Exploration Architectures. IEEE Trans Cogn Dev Syst 2020. [DOI: 10.1109/tcds.2018.2883249] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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11
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Hafner VV, Loviken P, Pico Villalpando A, Schillaci G. Prerequisites for an Artificial Self. Front Neurorobot 2020; 14:5. [PMID: 32153380 PMCID: PMC7046588 DOI: 10.3389/fnbot.2020.00005] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 01/17/2020] [Indexed: 12/11/2022] Open
Abstract
Traditionally investigated in philosophy, body ownership and agency-two main components of the minimal self-have recently gained attention from other disciplines, such as brain, cognitive and behavioral sciences, and even robotics and artificial intelligence. In robotics, intuitive human interaction in natural and dynamic environments becomes more and more important, and requires skills such as self-other distinction and an understanding of agency effects. In a previous review article, we investigated studies on mechanisms for the development of motor and cognitive skills in robots (Schillaci et al., 2016). In this review article, we argue that these mechanisms also build the foundation for an understanding of an artificial self. In particular, we look at developmental processes of the minimal self in biological systems, transfer principles of those to the development of an artificial self, and suggest metrics for agency and body ownership in an artificial self.
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Affiliation(s)
- Verena V Hafner
- Adaptive Systems Group, Computer Science Department, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Pontus Loviken
- Softbank Robotics, Paris, France
- Centre for Robotics and Neural Systems (CRNS), University of Plymouth, Plymouth, United Kingdom
| | - Antonio Pico Villalpando
- Adaptive Systems Group, Computer Science Department, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Guido Schillaci
- Adaptive Systems Group, Computer Science Department, Humboldt-Universität zu Berlin, Berlin, Germany
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
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12
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Legaspi R, He Z, Toyoizumi T. Synthetic agency: sense of agency in artificial intelligence. Curr Opin Behav Sci 2019. [DOI: 10.1016/j.cobeha.2019.04.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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13
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Mohan V, Bhat A, Morasso P. Muscleless motor synergies and actions without movements: From motor neuroscience to cognitive robotics. Phys Life Rev 2019; 30:89-111. [DOI: 10.1016/j.plrev.2018.04.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 04/12/2018] [Accepted: 04/16/2018] [Indexed: 10/17/2022]
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14
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Combined Sensing, Cognition, Learning, and Control for Developing Future Neuro-Robotics Systems: A Survey. IEEE Trans Cogn Dev Syst 2019. [DOI: 10.1109/tcds.2019.2897618] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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15
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Schürmann T, Mohler BJ, Peters J, Beckerle P. How Cognitive Models of Human Body Experience Might Push Robotics. Front Neurorobot 2019; 13:14. [PMID: 31031614 PMCID: PMC6470381 DOI: 10.3389/fnbot.2019.00014] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 03/21/2019] [Indexed: 01/08/2023] Open
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.
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Affiliation(s)
- Tim Schürmann
- Work and Engineering Psychology Research Group, Human Sciences, Technische Universität Darmstadt, Darmstadt, Germany
| | | | - Jan Peters
- Intelligent Autonomous Systems Group, Department of Computer Science, Technische Universität Darmstadt, Darmstadt, Germany.,Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Philipp Beckerle
- Elastic Lightweight Robotics, Department of Electrical Engineering and Information Technology, Robotics Research Institute, Technische Universität Dortmund, Dortmund, Germany.,Institute for Mechatronic Systems, Mechanical Engineering, Technische Universität Darmstadt, Darmstadt, Germany
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16
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Dürr V, Schilling M. Transfer of Spatial Contact Information Among Limbs and the Notion of Peripersonal Space in Insects. Front Comput Neurosci 2018; 12:101. [PMID: 30618693 PMCID: PMC6305554 DOI: 10.3389/fncom.2018.00101] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 12/03/2018] [Indexed: 11/13/2022] Open
Abstract
Internal representation of far-range space in insects is well established, as it is necessary for navigation behavior. Although it is likely that insects also have an internal representation of near-range space, the behavioral evidence for the latter is much less evident. Here, we estimate the size and shape of the spatial equivalent of a near-range representation that is constituted by somatosensory sampling events. To do so, we use a large set of experimental whole-body motion capture data on unrestrained walking, climbing and searching behavior in stick insects of the species Carausius morosus to delineate ‘action volumes’ and ‘contact volumes’ for both antennae and all six legs. As these volumes are derived from recorded sampling events, they comprise a volume equivalent to a representation of coinciding somatosensory and motor activity. Accordingly, we define this volume as the peripersonal space of an insect. It is of immediate behavioral relevance, because it comprises all potential external object locations within the action range of the body. In a next step, we introduce the notion of an affordance space as that part of peripersonal space within which contact-induced spatial estimates lie within the action ranges of more than one limb. Because the action volumes of limbs overlap in this affordance space, spatial information from one limb can be used to control the movement of another limb. Thus, it gives rise to an affordance as known for contact-induced reaching movements and spatial coordination of footfall patterns in stick insects. Finally, we probe the computational properties of the experimentally derived affordance space for pairs of neighboring legs. This is done by use of artificial neural networks that map the posture of one leg into a target posture of another leg with identical foot position.
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Affiliation(s)
- Volker Dürr
- Biological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany.,Cluster of Excellence Cognitive Interactive Technology (CITEC), Bielefeld University, Bielefeld, Germany
| | - Malte Schilling
- Cluster of Excellence Cognitive Interactive Technology (CITEC), Bielefeld University, Bielefeld, Germany
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17
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18
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Pointeau G, Dominey PF. The Role of Autobiographical Memory in the Development of a Robot Self. Front Neurorobot 2017; 11:27. [PMID: 28676751 PMCID: PMC5476692 DOI: 10.3389/fnbot.2017.00027] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2016] [Accepted: 05/22/2017] [Indexed: 11/13/2022] Open
Abstract
This article briefly reviews research in cognitive development concerning the nature of the human self. It then reviews research in developmental robotics that has attempted to retrace parts of the developmental trajectory of the self. This should be of interest to developmental psychologists, and researchers in developmental robotics. As a point of departure, one of the most characteristic aspects of human social interaction is cooperation-the process of entering into a joint enterprise to achieve a common goal. Fundamental to this ability to cooperate is the underlying ability to enter into, and engage in, a self-other relation. This suggests that if we intend for robots to cooperate with humans, then to some extent robots must engage in these self-other relations, and hence they must have some aspect of a self. Decades of research in human cognitive development indicate that the self is not fully present from the outset, but rather that it is developed in a usage-based fashion, that is, through engaging with the world, including the physical world and the social world of animate intentional agents. In an effort to characterize the self, Ulric Neisser noted that self is not unitary, and he thus proposed five types of self-knowledge that correspond to five distinct components of self: ecological, interpersonal, conceptual, temporally extended, and private. He emphasized the ecological nature of each of these levels, how they are developed through the engagement of the developing child with the physical and interpersonal worlds. Crucially, development of the self has been shown to rely on the child's autobiographical memory. From the developmental robotics perspective, this suggests that in principal it would be possible to develop certain aspects of self in a robot cognitive system where the robot is engaged in the physical and social world, equipped with an autobiographical memory system. We review a series of developmental robotics studies that make progress in this enterprise. We conclude with a summary of the properties that are required for the development of these different levels of self, and we identify topics for future research.
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Affiliation(s)
- Gregoire Pointeau
- Institut National de la Santé et de la Recherche Médicale, Stem Cell and Brain Research Institute U1208, Univ Lyon, Université Claude Bernard Lyon 1Lyon, France.,Robot Cognition Laboratory, Centre National de la Recherche ScientifiqueLyon, France
| | - Peter Ford Dominey
- Institut National de la Santé et de la Recherche Médicale, Stem Cell and Brain Research Institute U1208, Univ Lyon, Université Claude Bernard Lyon 1Lyon, France.,Robot Cognition Laboratory, Centre National de la Recherche ScientifiqueLyon, France
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