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REVIEW 4 major objections 6 minor 81 references

Mapping Human-Agent Co-Learning and Co-Adaptation: A Scoping Review

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A scoping review of human-agent co-learning finds that two-way adaptation, not one-sided adjustment, dominates the literature.

desk verdict Useful map, shaky numbers: the review's core percentages don't survive contact with its own tables. read the letter →

arxiv 2506.06324 v1 pith:H2MHBTGW submitted 2025-05-30 cs.AI

Shruti Kumar , Xiaoyu Chen , Xiaomei Wang This is my paper
classification cs.AI
keywords human-agentteamingco-learningco-adaptationmutualadaptationscopingreviewreinforcementlearninghuman-robotcollaborationcognitiveframeworks
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is a scoping review that sets out to organize a young, fragmented research area: humans and intelligent agents that learn from and adapt to each other. The authors screened records from three scholarly databases, included 77 studies, and coded each for adaptation style, agent type, task domain, cognitive framework, and performance measure. Their central finding is that two-way adaptation, where both human and agent adjust, dominates the literature, appearing in 84.14% of the coded papers, with reinforcement learning the most common agent method. They also claim that the vocabulary of 'co-learning,' 'co-adaptation,' 'mutual learning,' and related terms is used inconsistently, so the review's contribution is partly a shared map and partly a call for clearer definitions.

What carries the argument

The analytical engine is the adaptation-style classification: each study is assigned to two-way, one-way, both, or unspecified adaptation, and the results are counts of papers under those codes. Around that core code the authors also classify each study by intelligent-agent method, task domain, cognitive framework, and reported performance metric, using a spreadsheet-based synthesis with consensus discussion to resolve disagreements. The two-way code does the load-bearing work because it operationalizes the review's inclusion criterion: a paper counts as co-learning or co-adaptation only when both partners, not just the agent, are adjusting.

What would settle it

Re-code a random sample of the 77 included papers with a second coder using the same spreadsheet dimensions, then compute inter-rater agreement; if coders disagree on adaptation style, or if the 84.14% two-way share moves by more than a few points under re-coding, the headline percentages are not stable enough to build on.

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Extended reading notes

Core claim

On its own terms, the paper claims to be the first scoping review of human-agent co-learning and co-adaptation, and it claims that the literature is real but terminologically unsettled. It finds that 69 of 82 coded papers (84.14%) describe two-way adaptation, 5 describe one-way adaptation, 4 describe both, and 4 do not specify; that reinforcement learning is the most common intelligent-agent method, reported at 28.57%; and that decision-making, performance, trust, and mental models are the dominant cognitive themes. The paper also traces the field's timeline, crediting a 2002 mutual mind-reading study as the starting point and locating the first use of 'co-learning' in 2015, with the main growth spurt after 2021. If these claims are right, the field's center of gravity is mutual, two-way adjustment, and future systems and measurements should be built around that dyadic loop.

Load-bearing premise

The quantitative results presuppose that the authors' manual assignment of adaptation style and cognitive focus to each paper is reliable and consistent; the paper reports consensus discussion but no inter-rater reliability statistic and no public codebook.

Editorial extensions

If this is right

  • If the 84.14% two-way finding holds, co-learning and co-adaptation are the modal object of study, so future frameworks should target mutual, not one-sided, adjustment.
  • If reinforcement learning is truly the most common agent method, progress in RL-based co-learning tools will disproportionately shape the field's next phase.
  • If decision-making, trust, mental models, and performance are the dominant cognitive themes, evaluation instruments for co-learning should measure all four to be comparable with prior work.
  • If the terminology is as inconsistent as the review suggests, a shared ontology of 'co-' terms would unify reporting and enable future meta-analyses.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The coding may compress a continuum into categories: a paper labeled 'two-way' could still show asymmetric rates, with the human adapting far more than the agent, so future work could measure directionality rather than mere presence.
  • Because most included studies are lab-based with small samples, the map describes the literature, not the field's real-world effectiveness; transferring these patterns into deployed systems remains untested.
  • A testable extension: rerun the same search and coding on publications from 2024 onward to see whether the 84.14% two-way share and the reinforcement-learning dominance hold as generative AI enters the space.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. This paper reports a PRISMA-ScR scoping review of research on human-agent co-learning and co-adaptation. It states three research questions: RQ1 on terminology used for the relationship, RQ2 on agent types and task domains, and RQ3 on cognitive theories and frameworks. The authors searched Web of Science, Engineering Village, and EBSCOhost, screened 373 records, and report 77 (or sometimes 82) included papers. They present Table 1 characterizing each study and Table 2 listing AI methods and performance metrics; the Results give percentages for adaptation style, cognitive themes, methods, and geographic spread.

Significance. The paper addresses a genuinely emerging topic and would be a useful map if the quantitative claims were reliable. Its contributions include a structured PRISMA flow, two detailed extraction tables, and a first-pass synthesis of terminology. It does not claim a new formal model or derive predictions; its value is empirical synthesis. The main threat is that the headline percentages are internally inconsistent and the coding procedure is not auditable. Because these issues are correctable, the review's underlying contribution remains salvageable.

major comments (4)
  1. [Results, 'Human-Agent/AI Teams'; Figure 2; Table 1] The central quantitative claim is internally inconsistent. The text states 'among 82 papers reviewed, 69 (84.14%) focused on the two-way adaptation style,' but Figure 2 records 77 included studies and Table 1 is described as covering 77 reports. A later sentence in the same section says '69/77 (89.61%).' The 82-paper denominator appears to include the five one-way papers that Figure 2 lists as removed ('Removed papers with one-sided adaptation style (N=5)'). This is not cosmetic: 69/77 = 89.61%, not 84.14%, and if the inclusion criterion is two-way adaptation, one-way papers cannot be part of the reviewed set. The authors must reconcile the flow, the inclusion criteria, and all percentages using a single consistent corpus.
  2. [Results, 'Study Selection' and PRISMA flow] The PRISMA flow arithmetic does not add up. From 92 papers for full-text review, the flow says 1 inaccessible and 9 removed by eligibility, i.e., 10 removals, yet reports 77 included (92−10 = 82). If 5 one-sided papers are also removed, removals are 15 and included is 77. The text also says 'the authors removed 10.86% (10/92) of papers,' which is consistent with 82, not 77. These numbers must be corrected and aligned with the final inclusion set.
  3. [Results, 'Types of Agents and AI Algorithms' and Discussion] The most-used-algorithm claim contains an arithmetic error: 'Reinforcement Learning (21/77, 28.57%)' is impossible because 21/77 = 27.27%; 28.57% equals 22/77. The same section's country-region breakdown (Europe 39/77, Asia 26/77, North America 21/77, South America 1/77) sums to 87/77 = 113%, so the geographic percentages are overcounted; the authors need to decide whether multi-country papers are counted once or in each region and recompute.
  4. [Methods, 'Study data collection and synthesis'] The coding scheme underlying all prevalence figures is not documented. The manuscript reports Excel notes and consensus discussion but provides no codebook, no operational definition of 'two-way' versus 'one-way' coding (beyond the inclusion statement), no inter-rater reliability statistic, and no release of the extraction sheets. Because every percentage in Results depends on these manual judgments, the classification must be made reproducible (e.g., a codebook, dual-coding counts, and IRR) or the claims should be presented as illustrative rather than quantitative.
minor comments (6)
  1. [Methods, 'Inclusion and Exclusion Criteria'] Exclusion criterion #5 is self-contradictory: it is listed under 'We excluded studies with the following criteria' but says 'For comparison, the study included those that state co-adaptation... but describe only the agent's adaptation.' Clarify whether one-way papers were included for comparison, and if so, remove them from the excluded list and align the PRISMA flow.
  2. [Results, 'Human-Agent/AI Teams'] The citation for the 'first-ever study on mutual adaptation' is given as [66], which in the reference list is Kita et al. on autonomous assistive devices; the text later credits Yamada and Yamaguchi [20] with the 2002 foundation. Correct the reference or reconcile the attribution.
  3. [Results, 'Concept and functions of Human-Agent/AI Teams'] The sentence 'Yong describes the mutual adoption of gesture-based human-robot interfaces based on the Wizard of Oz experiment, which is a one-sided approach [19]' appears to mix authors and references; the cited [19] is De Santis (body-machine interfaces), not Yong, and the intended [62] is Xu et al. Check all bracketed citations against the reference list.
  4. [Table 1] Table 1 contains rows with 'NA' entries for adaptation style (e.g., Burke [9] and Damiano/Dumouchel [27]) even though the Results' 69/82 classification implies every row was coded; include a 'not reported' category explicitly in Table 1 and state how NA rows were handled.
  5. [Introduction and title] The title's 'Human-Agent' and the abstract's 'human-AI-robot' are not clearly distinguished; define the scope (humans with AI and/or robots) in the introduction to avoid terminological drift.
  6. [Throughout] There are several typographical and nomenclature errors, including 'Parlo [79]' (should likely be 'Palro'), 'ABIO [20]' (should be 'AIBO'), and 'mutual adoption' (should likely be 'mutual adaptation'). These should be corrected in a careful pass.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a descriptive scoping review whose percentages and thematic counts are summaries of an external literature, not quantities derived from its own definitions or self-citations.

full rationale

The paper's central claims are corpus descriptions: 77 studies included, adaptation-style proportions, cognitive-theme frequencies, and counts of agent/AI methods. There is no fitted parameter that is later relabeled as a prediction, and no derivation chain in which an output is constructed from its own input. The authors do not rely on their own prior results; the references to van Zoelen, van den Bosch, and Nikolaidis are external works, and none of the three authors appears to self-cite load-bearing claims. The definition of co-learning/co-adaptation is two-way adaptation, and the inclusion criteria require two-way learning, so the finding that most reviewed studies exhibit two-way adaptation is partly constrained by the selection criteria; however, the review actually coded a mix of two-way, one-way, both, and not-reported styles, so the reported distribution is an empirical coding outcome rather than a tautology. The internal inconsistency between the PRISMA flow/Table 1 (N=77) and the Results denominator (N=82) is a correctness/accounting flaw, not circularity: even a corrected percentage would remain a summary of the selected corpus. No circular steps were identified.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claims of this review rest on search coverage, on the reliability of the authors' manual coding, and on the borrowed definitions of co-learning and co-adaptation. There are no fitted numerical parameters and no invented entities. The review pays for its content through these assumptions, none of which are validated with reliability data or a comparison corpus.

assumptions (3)
  • domain assumption A three-database, English-only, peer-reviewed search before January 2024 is sufficient to capture the relevant co-learning and co-adaptation literature.
    The bibliometric shares, such as the 84% two-way adaptation figure, are descriptive of this corpus only. If additional databases or non-English work hold major streams, the percentages are incomplete. Invoked in Methods / Search Strategy.
  • domain assumption Manual labeling of adaptation style and cognitive frameworks is consistent across the 77 or 82 papers.
    The authors describe consensus discussions but report no codebook or inter-rater reliability. Every count in Results depends on these labels. Invoked in Methods / Study data collection and synthesis.
  • domain assumption The definitions of co-learning and co-adaptation from van Zoelen [39] and Nikolaidis [2] are the correct inclusion standard.
    Using different definitions would change which studies count as two-way co-learning and would alter the reported distributions. Invoked in Methods / Definition for Human-Intelligent Agent Co-learning/Co-adaptation.

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Cite this review

Pith. "Pith review of Mapping Human-Agent Co-Learning and Co-Adaptation: A Scoping Review." pith.science (2026). https://pith.science/paper/H2MHBTGW

@misc{pith2026250606324,
  author       = {Pith},
  title        = {Pith review of: Mapping Human-Agent Co-Learning and Co-Adaptation: A Scoping Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H2MHBTGW}},
  note         = {Machine review of arXiv:2506.06324}
}
read the original abstract

Several papers have delved into the challenges of human-AI-robot co-learning and co-adaptation. It has been noted that the terminology used to describe this collaborative relationship in existing studies needs to be more consistent. For example, the prefix "co" is used interchangeably to represent both "collaborative" and "mutual," and the terms "co-learning" and "co-adaptation" are sometimes used interchangeably. However, they can reflect subtle differences in the focus of the studies. The current scoping review's primary research question (RQ1) aims to gather existing papers discussing this collaboration pattern and examine the terms researchers use to describe this human-agent relationship. Given the relative newness of this area of study, we are also keen on exploring the specific types of intelligent agents and task domains that have been considered in existing research (RQ2). This exploration is significant as it can shed light on the diversity of human-agent interactions, from one-time to continuous learning/adaptation scenarios. It can also help us understand the dynamics of human-agent interactions in different task domains, guiding our expectations towards research situated in dynamic, complex domains. Our third objective (RQ3) is to investigate the cognitive theories and frameworks that have been utilized in existing studies to measure human-agent co-learning and co-adaptation. This investigation is crucial as it can help us understand the theoretical underpinnings of human-agent collaboration and adaptation, and it can also guide us in identifying any new frameworks proposed specifically for this type of relationship.

Figures

Figures reproduced from arXiv: 2506.06324 by the authors.

Figure 1
Figure 1. Terms used for the Conceptual framework of the scoping review [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 3
Figure 3. Comparing different adaptation styles for Human-Agent Teams. • Co-learning • Co-adaptation • Co-evolution • Mutual learning • Mutual adaptation • Hybrid Intelligence • Human-robot collaboration • Mutual adaptation Two-side adaptation (N= 69) Human-Agent Teams One-side adaptation (N=5) Cognitive Theories and framework (N=62) • Decision Making • Trust • Mental model • Performance [PITH_FULL_IMAGE:figures/full_fig_p02… view at source ↗

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Works this paper leans on

81 extracted references · 64 canonical work pages

  1. [1]

    Identifying Interaction Patterns of Tangible Co-Adaptations in Human-Robot Team Behaviors,

    E. M. van Zoelen, K. van den Bosch, M. Rauterberg, E. Barakova, and M. Neerincx, “Identifying Interaction Patterns of Tangible Co-Adaptations in Human-Robot Team Behaviors, ” Front Psychol, vol. 12, Jul. 2021, doi: 10.3389/fpsyg.2021.645545

    work page arXiv 2021

  2. [2]

    Human-robot mutual adaptation in collaborative tasks: Models and experiments,

    S. Nikolaidis, D. Hsu, and S. Srinivasa, “Human-robot mutual adaptation in collaborative tasks: Models and experiments, ” International Journal of Robotics Research, vol. 36, no. 5–7, pp. 618–634, Jun. 2017, doi: 10.1177/0278364917690593

    work page doi:10.1177/0278364917690593 2017

  3. [3]

    A Platform System for Developing a Collaborative Mutually Adaptive Agent

    Y . Xu et al., “A Platform System for Developing a Collaborative Mutually Adaptive Agent. ”

  4. [4]

    Active adaptation in human-agent collaborative interaction,

    Y . Xu et al., “Active adaptation in human-agent collaborative interaction, ” J Intell Inf Syst, vol. 37, no. 1, pp. 23–38, Aug. 2011, doi: 10.1007/s10844-010-0135-2

    work page doi:10.1007/s10844-010-0135-2 2011

  5. [5]

    I E E E, 2009

    2009 IEEE International Conference on Intelligent Computing and Intelligent Systems. I E E E, 2009

    work page 2009

  6. [6]

    Formation conditions of mutual adaptation in human-agent collaborative interaction,

    Y . Xu et al., “Formation conditions of mutual adaptation in human-agent collaborative interaction, ” Applied Intelligence, vol. 36, no. 1, pp. 208–228, Jan. 2012, doi: 10.1007/s10489-010-0255-y

    work page doi:10.1007/s10489-010-0255-y 2012

  7. [7]

    Perturbation Training for Human- Robot Teams F-C CI A uthor Signature redacted Signature redacted Chair, Department Committee on Graduate Students,

    R. Ramakrishnan, L. D. Co, / Lme, and A. Kolodziejski, “Perturbation Training for Human- Robot Teams F-C CI A uthor Signature redacted Signature redacted Chair, Department Committee on Graduate Students, ” 2015

    work page 2015

  8. [8]

    Symbiotic Co-Evolution in Collaborative Human- Machine Decision Making: Exploration of a Multi-Year Design Science Research Project in the Air Cargo Industry

    D. A. Döppner, P . Derckx, and D. Schoder, “Symbiotic Co-Evolution in Collaborative Human- Machine Decision Making: Exploration of a Multi-Year Design Science Research Project in the Air Cargo Industry. ” [Online]. Available: https://hdl.handle.net/10125/59467

    work page

Show all 81 references
  1. [9]

    Understanding team adaptation: A conceptual analysis and model,

    C. S. Burke, K. C. Stagl, E. Salas, L. Pierce, and D. Kendall, “Understanding team adaptation: A conceptual analysis and model, ” Journal of Applied Psychology, vol. 91, no. 6, pp. 1189– 1207, Nov. 2006, doi: 10.1037/0021-9010.91.6.1189

    2006 doi

  2. [10]

    Co-constructing Grounded Symbols—Feedback and Incremental Adaptation in Human–Agent Dialogue,

    H. Buschmeier and S. Kopp, “Co-constructing Grounded Symbols—Feedback and Incremental Adaptation in Human–Agent Dialogue, ” KI - Kunstliche Intelligenz, vol. 27, no. 2, pp. 137–143, May 2013, doi: 10.1007/s13218-013-0241-8

    2013 doi

  3. [11]

    Evaluating Fluency in Human-Robot Collaboration,

    G. Hoffman, “Evaluating Fluency in Human-Robot Collaboration, ” IEEE Trans Hum Mach Syst, vol. 49, no. 3, pp. 209–218, Jun. 2019, doi: 10.1109/THMS.2019.2904558

    2019

  4. [12]

    Accelerating Human-Agent Collaborative Reinforcement Learning,

    F . Lygerakis, M. Dagioglou, and V . Karkaletsis, “Accelerating Human-Agent Collaborative Reinforcement Learning, ” in ACM International Conference Proceeding Series, Association for Computing Machinery, Jun. 2021, pp. 90–92. doi: 10.1145/3453892.3454004

    2021

  5. [13]

    The Role of Adaptation in Collective Human–AI Teaming,

    M. Zhao, R. Simmons, and H. Admoni, “The Role of Adaptation in Collective Human–AI Teaming, ” Top Cogn Sci, 2022, doi: 10.1111/tops.12633

    2022 doi

  6. [14]

    Formalizing Human-Robot Mutual Adaptation: A Bounded Memory Model

    S. Nikolaidis, A. Kuznetsov, D. Hsu, and S. Srinivasa, “Formalizing Human-Robot Mutual Adaptation: A Bounded Memory Model. ”

  7. [15]

    Sociable dining table: Incremental meaning acquisition based on mutual adaptation process,

    K. Youssef, P . R. S. De Silva, and M. Okada, “Sociable dining table: Incremental meaning acquisition based on mutual adaptation process, ” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ...

    2014 doi

  8. [16]

    An Automated Metrics Set for Mutual Adaptation between Human and Robotic Device

    D. D. Damian, A. Hernandez-Arieta, M. Lungarella, and R. Pfeifer, “An Automated Metrics Set for Mutual Adaptation between Human and Robotic Device. ”

  9. [17]

    Promoting mutual adaptation in haptic negotiation using adaptive virtual fixture,

    H. Zhou, D. Wei, Y . Chen, and F . Wu, “Promoting mutual adaptation in haptic negotiation using adaptive virtual fixture, ” Industrial Robot, vol. 48, no. 2, pp. 313–326, 2020, doi: 10.1108/IR-07-2020-0142

    2020 doi

  10. [18]

    Open-end human-robot interaction from the dynamical systems perspective: mutual adaptation and incremental learning,

    T. Ogata, S. Sugano, and J. Tani, “Open-end human-robot interaction from the dynamical systems perspective: mutual adaptation and incremental learning, ” 2005

    2005

  11. [19]

    A Framework for Optimizing Co-adaptation in Body-Machine Interfaces,

    D. De Santis, “A Framework for Optimizing Co-adaptation in Body-Machine Interfaces, ” Front Neurorobot, vol. 15, Apr. 2021, doi: 10.3389/fnbot.2021.662181

    2021

  12. [20]

    lnt. Workshop on Robot and Human lnteractive Communication Mutual Adaptation to Mind Mapping in Human-Agent Interaction

    S. Yamada, “lnt. Workshop on Robot and Human lnteractive Communication Mutual Adaptation to Mind Mapping in Human-Agent Interaction. ”

  13. [21]

    Co-Adaptive Myoelectric Interface for Continuous Control∗,

    M. M. Madduri et al., “Co-Adaptive Myoelectric Interface for Continuous Control∗,” in IFAC- PapersOnLine, Elsevier B. V ., Dec. 2022, pp. 95–100. doi: 10.1016/j.ifacol.2023.01.109

    2022 doi

  14. [22]

    Interacting with robots to investigate the bases of social interaction,

    A. Sciutti and G. Sandini, “Interacting with robots to investigate the bases of social interaction, ” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 12, pp. 2295–2304, Dec. 2017, doi: 10.1109/TNSRE.2017.2753879

    2017

  15. [23]

    Machine Behavior Development and Analysis using Reinforcement Learning,

    Y . Gao, “Machine Behavior Development and Analysis using Reinforcement Learning, ” 1983. [Online]. Available: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-423434

    1983

  16. [24]

    IEEE Control Systems Society, 2019 IEEE 58th Conference on Decision and Control (CDC)

    2019

  17. [25]

    Classifiers and adaptable features improve myoelectric command accuracy in trained users,

    S. M. O’Meara, S. K. Robinson, and S. S. Joshi, “Classifiers and adaptable features improve myoelectric command accuracy in trained users, ” in International IEEE/EMBS Conference on Neural Engineering, NER, IEEE Computer Society, May 2021, pp. 924–927. doi: 10.1109/NER49283.20...

    2021

  18. [26]

    Data-based Iterative Human-in-the-loop Robot-Learning for Output Tracking,

    R. B. Warrier and S. Devasia, “Data-based Iterative Human-in-the-loop Robot-Learning for Output Tracking, ” Elsevier B. V ., Jul. 2017, pp. 12113–12118. doi: 10.1016/j.ifacol.2017.08.2142

    2017 doi

  19. [27]

    Anthropomorphism in human-robot co-evolution,

    L. Damiano and P . Dumouchel, “Anthropomorphism in human-robot co-evolution, ” Front Psychol, vol. 9, no. MAR, Mar. 2018, doi: 10.3389/fpsyg.2018.00468

    2018

  20. [28]

    Co-constructing knowledge with generative AI tools: Reflections from a CSCL perspective,

    U. Cress and J. Kimmerle, “Co-constructing knowledge with generative AI tools: Reflections from a CSCL perspective, ” Int J Comput Support Collab Learn, vol. 18, no. 4, pp. 607–614, Dec. 2023, doi: 10.1007/s11412-023-09409-w

    2023 doi

  21. [29]

    Robocamp at home: Exploring families’ co- learning with a social robot: Findings from a one-month study in the wild,

    A. Ahtinen, N. Beheshtian, and K. Väänänen, “Robocamp at home: Exploring families’ co- learning with a social robot: Findings from a one-month study in the wild, ” in ACM/IEEE International Conference on Human-Robot Interaction, IEEE Computer Society, Mar. 2023, pp. 331–340. d...

    2023

  22. [30]

    Mutual adaptation between a human and a robot based on timing control of ‘Sleep-time, ’

    M. Kitagawa, B. L. Evans, N. Munekata, and T. Ono, “Mutual adaptation between a human and a robot based on timing control of ‘Sleep-time, ’” in HAI 2016 - Proceedings of the 4th International Conference on Human Agent Interaction, Association for Computing Machinery, Inc, Oct....

    2016

  23. [31]

    IEEE Robotics and Automation Society and Institute of Electrical and Electronics Engineers, ICRA2017 : IEEE International Conference on Robotics and Automation : program : May 29- June 3, 2017, Singapore

    2017

  24. [32]

    2008, Technische Universität München, Munich, Germany

    Institute of Electrical and Electronics Engineers, IEEE International Symposium on Robot and Human Interactive Communication 17 2008.08.01-03 Munich, and RO-MAN 17 2008.08.01-03 Munich, The 17th IEEE International Symposium on Robot and Human Interactive Communication, 2008 RO...

    2008

  25. [33]

    Human-agent co-adaptation using error-related potentials,

    S. K. Ehrlich and G. Cheng, “Human-agent co-adaptation using error-related potentials, ” J Neural Eng, vol. 15, no. 6, Sep. 2018, doi: 10.1088/1741-2552/aae069

    2018 doi

  26. [34]

    Brain computer interface to distinguish between self and other related errors in human agent collaboration,

    V . Dimova-Edeleva, S. K. Ehrlich, and G. Cheng, “Brain computer interface to distinguish between self and other related errors in human agent collaboration, ” Sci Rep, vol. 12, no. 1, Dec. 2022, doi: 10.1038/s41598-022-24899-8

    2022 doi

  27. [35]

    Adaptive Fuzzy Neural Agent for Human and Machine Co-learning,

    C. S. Lee, Y . L. Tsai, M. H. Wang, S. H. Huang, M. Reformat, and N. Kubota, “Adaptive Fuzzy Neural Agent for Human and Machine Co-learning, ” International Journal of Fuzzy Systems, vol. 24, no. 2, pp. 778–798, Mar. 2022, doi: 10.1007/s40815-021-01188-6

    2022 doi

  28. [36]

    Bridging Human-Robot Co- Adaptation via Biofeedback for Continuous Myoelectric Control,

    X. Hu, A. Song, H. Zeng, Z. Wei, H. Deng, and D. Chen, “Bridging Human-Robot Co- Adaptation via Biofeedback for Continuous Myoelectric Control, ” IEEE Robot Autom Lett, vol. 8, no. 12, pp. 8573–8580, Dec. 2023, doi: 10.1109/LRA.2023.3330053

    2023

  29. [37]

    FML-Based Reinforcement Learning Agent with Fuzzy Ontology for Human- Robot Cooperative Edutainment,

    C. S. Lee et al., “FML-Based Reinforcement Learning Agent with Fuzzy Ontology for Human- Robot Cooperative Edutainment, ” International Journal of Uncertainty, Fuzziness and Knowldege-Based Systems, vol. 28, no. 6, pp. 1023–1060, Dec. 2020, doi: 10.1142/S0218488520500440

    2020 doi

  30. [38]

    Six Challenges for Human-AI Co-learning,

    K. van den Bosch, T. Schoonderwoerd, R. Blankendaal, and M. Neerincx, “Six Challenges for Human-AI Co-learning, ” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 2019, pp....

    2019 doi

  31. [39]

    Becoming Team Members: Identifying Interaction Patterns of Mutual Adaptation for Human-Robot Co-Learning,

    E. M. van Zoelen, K. van den Bosch, and M. Neerincx, “Becoming Team Members: Identifying Interaction Patterns of Mutual Adaptation for Human-Robot Co-Learning, ” Front Robot AI, vol. 8, Jul. 2021, doi: 10.3389/frobt.2021.692811

    2021

  32. [40]

    Individualized Mutual Adaptation in Human-Agent Teams,

    H. Li et al., “Individualized Mutual Adaptation in Human-Agent Teams, ” IEEE Trans Hum Mach Syst, vol. 51, no. 6, pp. 706–714, Dec. 2021, doi: 10.1109/THMS.2021.3107675

    2021

  33. [41]

    Ontology-Based Reflective Communication for Shared Human-AI Recognition of Emergent Collaboration Patterns,

    E. M. van Zoelen, K. van den Bosch, D. Abbink, and M. Neerincx, “Ontology-Based Reflective Communication for Shared Human-AI Recognition of Emergent Collaboration Patterns, ” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and...

    2023 doi

  34. [42]

    Co-adaptive Human-Robot Cooperation: Summary and Challenges,

    S. Ahlberg et al., “Co-adaptive Human-Robot Cooperation: Summary and Challenges, ” 2022

    2022

  35. [43]

    Real-World Human-Robot Collaborative Reinforcement Learning,

    A. Shafti, J. Tjomsland, W. Dudley, and A. A. Faisal, “Real-World Human-Robot Collaborative Reinforcement Learning, ” Mar. 2020, [Online]. Available: http://arxiv.org/abs/2003.01156

    2020 arXiv

  36. [44]

    IEEE Communications Society, M. IEEE Systems, and Institute of Electrical and Electronics Engineers, 2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) : 27-31 March 2017

    2017

  37. [45]

    Cognitive Architecture for Co-Evolutionary Hybrid Intelligence,

    K. Krinkin and Y . Shichkina, “Cognitive Architecture for Co-Evolutionary Hybrid Intelligence, ” Sep. 2022, [Online]. Available: http://arxiv.org/abs/2209.12623

    2022 arXiv

  38. [46]

    Human-machine co-intelligence through symbiosis in the SMV space,

    Y . Yao, “Human-machine co-intelligence through symbiosis in the SMV space, ” Applied Intelligence, vol. 53, no. 3, pp. 2777–2797, Feb. 2023, doi: 10.1007/s10489-022-03574-5

    2023 doi

  39. [47]

    Ansari, P

    F . Ansari, P . Hold, W. Mayrhofer, S. Schlund, and W. Sihn, AUTODIDACT: INTRODUCING THE CONCEPT OF MUTUAL LEARNING INTO A SMART FACTORY INDUSTRY 4.0. 2018

    2018

  40. [48]

    Making friends on the fly: Cooperating with new teammates,

    S. Barrett, A. Rosenfeld, S. Kraus, and P . Stone, “Making friends on the fly: Cooperating with new teammates, ” Artif Intell, vol. 242, pp. 132–171, Jan. 2017, doi: 10.1016/j.artint.2016.10.005

    2017 doi

  41. [49]

    Human-Robot Co-Adaptation in Construction: Bio-Signal Based Control of Bricklaying Robots,

    Y . Liu and H. Jebelli, “Human-Robot Co-Adaptation in Construction: Bio-Signal Based Control of Bricklaying Robots, ” in Computing in Civil Engineering 2021 - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2021, American Society of Civ...

    2021 doi

  42. [50]

    Learning and Leveraging Conventions in the Design of Haptic Shared Control Paradigms for Steering a Ground Vehicle,

    V . Izadi and A. H. Ghasemi, “Learning and Leveraging Conventions in the Design of Haptic Shared Control Paradigms for Steering a Ground Vehicle, ” Int J Control Autom Syst, vol. 21, no. 10, pp. 3324–3335, Oct. 2023, doi: 10.1007/s12555-022-0509-6

    2023 doi

  43. [51]

    Improving Human-Robot Collaborative Reinforcement Learning through Probabilistic Policy Reuse,

    A. C. Tsitos, “Improving Human-Robot Collaborative Reinforcement Learning through Probabilistic Policy Reuse, ” 2022

    2022

  44. [52]

    Developing Team Design Patterns for Hybrid Intelligence Systems,

    E. Van Zoelen et al., “Developing Team Design Patterns for Hybrid Intelligence Systems, ” in Frontiers in Artificial Intelligence and Applications, IOS Press BV , Jun. 2023, pp. 3–16. doi: 10.3233/FAIA230071

    2023 doi

  45. [53]

    ‘Can You Guess My Moves?’ Playing Charades with a Humanoid Robot Employing Mutual Learning with Emotional Intelligence,

    B. Xie and C. H. Park, “ ‘Can You Guess My Moves?’ Playing Charades with a Humanoid Robot Employing Mutual Learning with Emotional Intelligence, ” in ACM/IEEE International Conference on Human-Robot Interaction, IEEE Computer Society, Mar. 2023, pp. 667–671. doi: 10.1145/35682...

    2023

  46. [54]

    2013, Tokyo, Japan ; conference digest

    IEEE Robotics and Automation Society, IEEE Industrial Electronics Society, Nihon Robotto Gakkai, Annual IEEE Computer Conference, IEEE/RSJ International Conference on Intelligent Robots and Systems 2013.11.03-07 Tokyo, and IROS 2013.11.03-07 Tokyo, 2013 IEEE/RSJ International ...

    2013

  47. [55]

    Co-Learning around Social Robots with School Pupils and University Students - Focus on Data Privacy Considerations,

    A. Ahtinen, A. Chowdhury, V . Ramírez Millan, C. H. Wu, and G. Menon, “Co-Learning around Social Robots with School Pupils and University Students - Focus on Data Privacy Considerations, ” in ACM International Conference Proceeding Series, Association for Computing Machinery, ...

    2023

  48. [56]

    Human-robot Co-learning for fluent collaborations,

    E. M. Van Zoelen, K. Van Den Bosch, and M. Neerincx, “Human-robot Co-learning for fluent collaborations, ” in ACM/IEEE International Conference on Human-Robot Interaction, IEEE Computer Society, Mar. 2021, pp. 574–576. doi: 10.1145/3434074.3446354

    2021

  49. [57]

    Physical human-robot interaction: Mutual learning and adaptation,

    S. Ikemoto, H. Ben Amor, T. Minato, B. Jung, and H. Ishiguro, “Physical human-robot interaction: Mutual learning and adaptation, ” IEEE Robot Autom Mag, vol. 19, no. 4, pp. 24– 35, 2012, doi: 10.1109/MRA.2011.2181676

    2012

  50. [58]

    A Shared Control Framework for Human- Multirobot Foraging with Brain-Computer Interface,

    W. Dai, Y . Liu, H. Lu, Z. Zheng, and Z. Zhou, “A Shared Control Framework for Human- Multirobot Foraging with Brain-Computer Interface, ” IEEE Robot Autom Lett, vol. 6, no. 4, pp. 6305–6312, Oct. 2021, doi: 10.1109/LRA.2021.3092290

    2021

  51. [60]

    Models and Measures of Human–Computer Symbiosis,

    R. Murray-Smith, “Models and Measures of Human–Computer Symbiosis, ” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8820, 2014, doi: 10.1007/978-3-319-13500-7

    2014 doi

  52. [61]

    A framework to design smart manufacturing systems for Industry 5.0 based on the human-automation symbiosis,

    M. Peruzzini, E. Prati, and M. Pelicciari, “A framework to design smart manufacturing systems for Industry 5.0 based on the human-automation symbiosis, ” Int J Comput Integr Manuf, 2023, doi: 10.1080/0951192X.2023.2257634

    2023

  53. [62]

    An experiment study of gesture-based human-robot interface

    Y . Xu, M. Guillemot, and T. Nishida, “An experiment study of gesture-based human-robot interface. ”

  54. [63]

    Shared Autonomy Locomotion Synthesis with a Virtual Powered Prosthetic Ankle,

    B. K. Hodossy and D. Farina, “Shared Autonomy Locomotion Synthesis with a Virtual Powered Prosthetic Ankle, ” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 4738–4748, 2023, doi: 10.1109/TNSRE.2023.3336713

    2023

  55. [64]

    Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaboration,

    S. Nikolaidis, S. Nath, A. D. Procaccia, and S. Srinivasa, “Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaboration, ” in ACM/IEEE International Conference on Human-Robot Interaction, IEEE Computer Society, Mar. 2017, pp. 323–331. doi: 10.1145/2909824.3020253

    2017

  56. [65]

    Institute of Electrical and Electronics Engineers, 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)

    2019

  57. [66]

    Development of Autonomous Assistive Devices- Analysis of change of human motion patterns

    K. Kita, R. Kato, H. Yokoi, and T. Arai, “Development of Autonomous Assistive Devices- Analysis of change of human motion patterns. ”

  58. [67]

    Modeling and Enhancing Human-Machine Interaction for Accessibility and Health

    M. Yamagami, “Modeling and Enhancing Human-Machine Interaction for Accessibility and Health. ”

  59. [68]

    Unpacking Human and AI Complementarity: Insights from Recent Works

    Y . Ren, X. Deng, and K. Joshi, “Unpacking Human and AI Complementarity: Insights from Recent Works. ”

  60. [69]

    Köppen, Machine Intelligence Research Labs, Kyūshū Daigaku

    M. Köppen, Machine Intelligence Research Labs, Kyūshū Daigaku. Research Center for Applied Perceptual Science, M. IEEE Systems, Kyūshū Daigaku, and Institute of Electrical and Electronics Engineers, Proceedings of the 2015 Seventh International Conference of Soft Computing...

    2015

  61. [70]

    A human-ai collaborative approach for clinical decision making on rehabilitation assessment,

    M. H. Lee, D. P . Siewiorek, and A. Smailagic, “A human-ai collaborative approach for clinical decision making on rehabilitation assessment, ” in Conference on Human Factors in Computing Systems - Proceedings, Association for Computing Machinery, May 2021. doi: 10.1145/3411764.3445472

    2021

  62. [71]

    Understanding Collective Intelligence: Investigating the Role of Collective Memory, Attention, and Reasoning Processes

    A. W. Woolley and P . Gupta, “Understanding Collective Intelligence: Investigating the Role of Collective Memory, Attention, and Reasoning Processes. ”

  63. [72]

    The FATE System Iterated: Fair, Transparent and Explainable Decision Making in a Juridical Case,

    M. H. T. De Boer et al., “The FATE System Iterated: Fair, Transparent and Explainable Decision Making in a Juridical Case, ” 2022. [Online]. Available: http://ceur-ws.org

    2022

  64. [73]

    Co-evolutionary hybrid intelligence

    K. Krinkin, Y . Shichkina, and A. Ignatyev, “Co-evolutionary hybrid intelligence. ”

  65. [74]

    An adaptive robot teacher boosts a human partner’s learning performance in joint action

    A. Vignolo, H. Powell, L. Mcellin, F . Rea, A. Sciutti, and J. Michael, “An adaptive robot teacher boosts a human partner’s learning performance in joint action. ”

  66. [75]

    Progressive Co-adaptation in Human-Machine Interaction

    P . Gallina, N. Bellotto, and M. Di Luca, “Progressive Co-adaptation in Human-Machine Interaction. ”

  67. [76]

    Mutual Shaping in the Design of Socially Assistive Robots: A Case Study on Social Robots for Therapy,

    K. Winkle, P . Caleb-Solly, A. Turton, and P . Bremner, “Mutual Shaping in the Design of Socially Assistive Robots: A Case Study on Social Robots for Therapy, ” Int J Soc Robot, vol. 12, no. 4, pp. 847–866, Aug. 2020, doi: 10.1007/s12369-019-00536-9

    2020 doi

  68. [77]

    October 6-9, 2019

    Institute of Electrical and Electronics Engineers, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) : Bari, Italy. October 6-9, 2019

    2019

  69. [78]

    A Human-Robot Mutual Learning System with Affect-Grounded Language Acquisition and Differential Outcomes Training,

    A. Markelius et al., “A Human-Robot Mutual Learning System with Affect-Grounded Language Acquisition and Differential Outcomes Training, ” Oct. 2023, [Online]. Available: http://arxiv.org/abs/2310.13377

    2023 arXiv

  70. [79]

    Intelligent agent for real-world applications on robotic edutainment and humanized co-learning,

    C. S. Lee et al., “Intelligent agent for real-world applications on robotic edutainment and humanized co-learning, ” J Ambient Intell Humaniz Comput, vol. 11, no. 8, pp. 3121–3139, Aug. 2020, doi: 10.1007/s12652-019-01454-4

    2020 doi

  71. [80]

    Boundary-Crossing Robots: Societal Impact of Interactions with Socially Capable Autonomous Agents,

    K. Jokinen and K. Watanabe, “Boundary-Crossing Robots: Societal Impact of Interactions with Socially Capable Autonomous Agents, ” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, ...

    2019 doi

  72. [81]

    Design patterns for human-AI co-learning: A wizard-of-Oz evaluation in an urban-search-and-rescue task,

    T. A. J. Schoonderwoerd, E. M. van Zoelen, K. van den Bosch, and M. A. Neerincx, “Design patterns for human-AI co-learning: A wizard-of-Oz evaluation in an urban-search-and-rescue task, ” International Journal of Human Computer Studies, vol. 164, Aug. 2022, doi: 10.1016/j.ijhc...

    2022

  73. [82]

    IEEE, 2017

    2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids). IEEE, 2017

    2017

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.