Pith. sign in

REVIEW 3 major objections 6 minor 141 references

Embodied Intelligence: The Key to Unblocking Generalized Artificial Intelligence

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper argues that closed-loop embodied interaction is essential for artificial general intelligence, not merely an optional enhancement.

desk verdict Readable survey of embodied AI that overclaims a necessity result; useful overview with fixable but real reliability issues. read the letter →

arxiv 2505.06897 v1 pith:4IEZWIB5 submitted 2025-05-11 cs.AI

Jinhao Jiang , Changlin Chen , Shile Feng , Wanru Geng , Zesheng Zhou , Ni Wang , Shuai Li , Feng-Qi Cui
show 1 more author
Erbao Dong
This is my paper · ORCID
classification cs.AI
keywords embodiedintelligenceartificialgeneralclosed-looparchitectureperceptionmoduledecision-makingfeedbackAGIprinciplesmodularvsend-to-end
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 argues that general artificial intelligence cannot be reached by computation alone; it requires a system physically present in the world, learning through real-time interaction. The authors organize embodied intelligence into four core modules—perception, decision-making, action, and feedback—and claim that when these form a closed loop, they jointly deliver the six capabilities that DeepMind's AGI principles demand: generalizability, performance, cognitive and metacognitive tasks, potential over deployment, ecological validity, and a viable development path. The paper's contribution is a systematic framework connecting embodied AI research to the AGI goal, where dynamic learning and real-world interaction bridge the gap between narrow AI and AGI. A reader should care because the argument reframes AGI progress around physical bodies and closed-loop adaptation rather than scale alone.

What carries the argument

The central object is the closed-loop modular architecture of embodied intelligence, decomposed into four components: perception (multimodal sensor fusion), intelligent decision-making (environmental understanding, task planning, decision generation, and a learning-and-evolution framework), action (motion control and feedback adjustment), and feedback (perceptual, decision, and action feedback). The loop is the load-bearing mechanism: feedback re-enters perception and decision-making so that behavior and cognition are continuously reshaped by the environment. The paper maps these modules onto the six AGI principles adopted from the 'Levels of AGI' framework, using that mapping as the bridge between EAI and AGI.

What would settle it

Find an intelligent system that achieves the six AGI principles—as operationalized by Morris et al. (2023)—while having no physical body and no real-time interaction loop with an environment (for example, a purely offline-trained model scored on embodied benchmarks), or show that an embodied agent with the feedback module removed still reaches the same level of generalization; either result would falsify the claim that embodiment is essential.

Watch

Extended reading notes

Core claim

The paper's central claim is that embodiment is a necessary condition for AGI. It proposes a modular closed-loop architecture—perception gathers multimodal sensory data, decision-making plans and generates actions, action executes motion through the physical body, and feedback monitors outcomes and optimizes the loop—and argues that this loop is the mechanism by which a system can satisfy the six AGI principles formulated by DeepMind: focusing on capabilities, generality, cognitive/metacognitive tasks, potential, ecological validity, and a long-term development path. For each principle, the paper identifies which module or module interaction operationalizes it, concluding that the integration of dynamic learning and real-world interaction is what separates AGI from narrow AI.

Load-bearing premise

The argument rests on the assumption that the four-module perception–decision–action–feedback decomposition is a faithful and complete representation of embodied intelligence, and that DeepMind's six AGI principles are the right yardstick for AGI; if either fails, the mapping is one contingent framing rather than a systematic finding.

Editorial extensions

If this is right

  • If embodiment is essential, AGI research should concentrate on real-time physical interaction and closed-loop learning rather than scaling static datasets alone.
  • A system that lacks a feedback module—one that perceives and decides but does not monitor and correct its own actions—would fall short of AGI under the paper's criteria.
  • Modular embodied architectures will remain a viable route to AGI, particularly where interpretability and independent module optimization matter, even as end-to-end systems push toward global optimization.
  • Progress toward AGI should be evaluated on embodied, ecologically valid tasks that exercise the full loop, not only on text or image benchmarks.

Reading between the lines

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

  • If the embodied thesis is correct, performance on physically interactive, closed-loop tasks should be a stronger predictor of AGI capability than performance on passive recognition or generation benchmarks—this is a testable prediction the authors imply but do not state.
  • The paper's four-module taxonomy suggests a concrete design test: an architecture that removes or weakens any one module (for instance, an open-loop action module) should show a measurable ceiling in transfer and generalization, a comparison the field could run on existing robot benchmarks.
  • The modular-versus-end-to-end framing implies a future hybrid—end-to-end perception-to-action cores augmented by explicit feedback pathways—rather than a winner-take-all outcome between the two paradigms.
  • The DeepMind six-principle mapping could be operationalized into a checklist scoring embodied systems, turning a conceptual argument into an evaluation rubric.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. This manuscript is a review-style position paper arguing that Embodied Artificial Intelligence (EAI) is the essential bridge from narrow AI to AGI. It proposes a technical taxonomy dividing EAI into end-to-end and modular architectures, then analyzes the modular architecture through four components (perception, decision-making, action, feedback). The paper maps each module onto DeepMind's six AGI principles, surveys recent techniques and industrial trends, and concludes that EAI's integration of dynamic learning and real-world interaction is essential for AGI. The paper contains no new experiments or derivations; its contribution is a conceptual framework and a broad literature survey.

Significance. If the central thesis were established, the paper would provide a useful organizing framework for AGI research: it offers a clear modular decomposition, a rich collection of recent references, and a concrete mapping between technical modules and AGI evaluation principles. The taxonomy of end-to-end versus modular architectures and the detailed module-by-module discussion are valuable reference material, and the paper is explicit about several open challenges. However, the load-bearing claim that physical embodiment is essential for AGI is not derived from the evidence presented; the paper's own analyses support only the weaker conclusion that EAI is one promising pathway. The significance is therefore conditional on a substantial reframing and additional argumentation.

major comments (3)
  1. [Abstract; Sections 4 and 6] The abstract's claim that EAI's integration of dynamic learning and real-world interaction is "essential" for AGI is not supported by the body of the paper. Sections 4.1-4.4 show at most that each modular component can contribute to or "align with" one or more of DeepMind's six principles; they do not rule out non-embodied systems that satisfy the same capability-oriented criteria. Indeed, Section 4 itself notes that DeepMind's principles focus on model capabilities, not processes, and physical embodiment is an implementation attribute. To support a necessity claim, the paper would need either a comparative analysis of non-embodied agents (e.g., tool-using language models operating in digital environments) or an explicit argument for why the capability criteria cannot be met without physical interaction. Without such an argument, the strongest conclusion available is that EAI is a promising pathway toward AGI, and the manuscript should be revised to state that conclusion rather than the current 'essential' claim.
  2. [Section 3.1 and Section 4] The paper defines two EAI paradigms, end-to-end and modular, in Section 3.1, but Section 4 analyzes only the modular four-component architecture in relation to the six AGI principles. End-to-end systems are described in Section 3.2 as major industrial approaches, yet their connection to AGI principles is never examined. Consequently, even the weaker claim that EAI contributes to AGI is incomplete: the analysis covers only one of the two paradigms that the paper itself identifies. Either the AGI mapping must be extended to end-to-end architectures, or the scope of the contribution claim should be explicitly limited to modular EAI.
  3. [Section 3.2.3 and Section 2.4] Several factual claims that support the narrative are unsupported or appear inaccurate. Section 3.2.3 states that Volkswagen's 'digital twin' pipelines achieve "78% cross-domain policy transferability" without any citation or methodological detail; as written, this is an unverifiable numerical assertion. Similarly, Section 2.4 attributes to reference [28] the development of "physics-informed neural controllers capable of adapting to environmental perturbations within 200ms latency," but reference [28] is a self-supervised correspondence paper for model-based reinforcement learning and does not appear to contain this claim. These unsupported numbers undermine the paper's reliability as a survey and should be either properly sourced and explained or removed.
minor comments (6)
  1. [Section 1 (Introduction)] The roadmap at the end of Section 1 is inconsistent with the actual structure: it states that Section 4 discusses future trends and challenges and Section 5 summarizes, whereas in the manuscript Section 4 covers the four modules, Section 5 covers future prospects and challenges, and Section 6 is the conclusion.
  2. [Section 4.1] The text describes the perceptual process as comprising "six critical steps," while the Figure 3 caption says "five steps" and lists only five items; the count and the caption should be reconciled.
  3. [Section 2.4] The three "fundamental advancements" listed in Section 2.4 are stated without supporting citations; given that the paper is a survey, each bullet should be accompanied by a specific reference.
  4. [Section 4.3] The phrase "The principle of autonomy is is demonstrated in this process" contains a duplicated word and a typo; the sentence should be rewritten.
  5. [Table 3] The column header "Innovative Industries" appears to be a mistranslation; it likely should read "Innovative Methods" or "Innovative Technologies." Additionally, the table lists year information in the same column as the method name, which is visually confusing.
  6. [Section 2.5] The prose in Section 2.5 (for example, "dialectical synthesis of symbolic priors and physical instantiation") is considerably more speculative and abstract than the rest of the survey, and would benefit from concrete examples or pointers to specific systems that instantiate these claims.

Circularity Check

1 steps flagged · score 3.0 of 10

The paper's strongest claim is self-confirming: it defines AGI by environmental interaction and EAI by environmental interaction, then concludes EAI is essential for AGI.

  1. self definitional [Section 1 (EAI definition), Section 2.2 / Table 1 (AGI operational feature), Abstract and Section 6 (conclusion)]
    "Table 1 lists AGI's operational feature as 'Self-learning through interaction with the environment' while ANI 'Runs through a fixed programming framework'. Section 1 defines 'Embodied intelligence (EAI) ... a system in which an agent perceives, learns and makes decisions through the interaction between its body and its environment.' The abstract concludes: 'EAI's integration of dynamic learning and real-world interaction is essential for bridging the gap between narrow AI and AGI.'"

    The necessity claim is obtained by construction from the paper's own definitions. If AGI's defining operational feature is 'self-learning through interaction with the environment' and EAI is defined as perceiving/learning/deciding through body-environment interaction, then 'EAI is essential for AGI' is already contained in the premises. Section 4's module-to-principle mapping does not supply independent evidence for this necessity: the adopted DeepMind criteria are capability-focused and process-agnostic, so they cannot by themselves force a physical-embodiment requirement. The survey organizes embodied AI research in a useful way, but the 'essential' verdict is a definitional consequence of the chosen AGI characterization rather than a result derived from the surveyed evidence.

full rationale

This manuscript is a review, not a quantitative derivation, and it contains no fitted-parameter predictions and no load-bearing self-citations: the references are external (e.g., DeepMind's 'Levels of AGI' [29], Brooks, Pfeifer and Scheier). The only significant circularity is definitional: AGI is characterized in Section 2.2 by 'self-learning through interaction with the environment,' and EAI is defined in Section 1 as perception, learning, and decision-making through body-environment interaction; the abstract then asserts interaction is essential for AGI. That makes the headline conclusion true by stipulation rather than by independent evidence. The four-module survey has genuinely independent descriptive content and is not manufactured, but the mapping in Section 4 does not establish a necessity claim because the adopted six-principle yardstick is process-agnostic and because only the modular architecture is analyzed, leaving end-to-end EAI unexamined in that mapping. These are assessment gaps that belong to correctness risk, not to circularity. On balance, the paper is mildly self-confirming in its framing but not systematically circular, so a low-to-moderate score of 3 is appropriate.

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

This is a review/position paper with no mathematical derivation. It introduces no free parameters and no invented entities. Its arguments rest on domain assumptions about taxonomies and evaluation criteria rather than on axioms.

assumptions (3)
  • domain assumption The four-module decomposition (perception, decision-making, action, feedback) is a faithful and complete description of embodied intelligence systems.
    Introduced in Section 3.1 and Figure 2 without evidence that all EAI architectures fit this decomposition.
  • domain assumption DeepMind's six AGI principles, adopted from reference [29], are the appropriate criteria for evaluating progress toward AGI.
    Adopted in Section 4 without justification; the paper's mapping of modules to AGI depends on this choice.
  • domain assumption The cited references support the specific factual claims made in the text.
    Several claims appear unsupported or mismatched, including the 200ms latency claim in Section 2.4, the 78% transferability figure in Section 3.2.3, and the Forward-Forward description in Section 4.2.4.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Embodied Intelligence: The Key to Unblocking Generalized Artificial Intelligence." pith.science (2026). https://pith.science/paper/4IEZWIB5

@misc{pith2026250506897,
  author       = {Pith},
  title        = {Pith review of: Embodied Intelligence: The Key to Unblocking Generalized Artificial Intelligence},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4IEZWIB5}},
  note         = {Machine review of arXiv:2505.06897}
}
read the original abstract

The ultimate goal of artificial intelligence (AI) is to achieve Artificial General Intelligence (AGI). Embodied Artificial Intelligence (EAI), which involves intelligent systems with physical presence and real-time interaction with the environment, has emerged as a key research direction in pursuit of AGI. While advancements in deep learning, reinforcement learning, large-scale language models, and multimodal technologies have significantly contributed to the progress of EAI, most existing reviews focus on specific technologies or applications. A systematic overview, particularly one that explores the direct connection between EAI and AGI, remains scarce. This paper examines EAI as a foundational approach to AGI, systematically analyzing its four core modules: perception, intelligent decision-making, action, and feedback. We provide a detailed discussion of how each module contributes to the six core principles of AGI. Additionally, we discuss future trends, challenges, and research directions in EAI, emphasizing its potential as a cornerstone for AGI development. Our findings suggest that EAI's integration of dynamic learning and real-world interaction is essential for bridging the gap between narrow AI and AGI.

Figures

Figures reproduced from arXiv: 2505.06897 by the authors.

Figure 1
Figure 1. The concept of embodied intelligence has gone through three important stages of development, culminating in today’s definition; the concept embodies three fundamental principles for system-level architectural design. Although the concept was once marginalized, with breakthroughs in robotics, reinforcement learning, and multimodal learning, embodied intelligence has regained widespread attention in the context of the… view at source ↗
Figure 2
Figure 2. An intelligent agent is composed of four major modules: the decision-making module, the perception module, the action module, and the feedback module[30][31][32][33][34][35][36]. The feedback module can conduct real-time monitoring and accumulate experience for the perception module, enabling the perception module to collect real-time environmental information, transmit the information to the decision-making module,… view at source ↗
Figure 3
Figure 3. The perception process of multimodal models typically involves five steps: The first step is data acquisition [41][42][43], which aims to collect multidimensional perceptual data through various sensors; the second step is data preprocessing [44][45][46], where the collected raw data undergoes cleaning, noise reduction, alignment, and other processes to ensure consistency across modalities; the third step is feature… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: The decision-making module comprises four main functional components: environmental understanding and rea￾soning, task planning, decision generation, and learning and evolution. The environmental understanding and reasoning module [67][68] extracts critical information…
Figure 5
Figure 5. Figure 5: The three main functions under the decision module are environment understanding and reasoning, task planning and decision generation. Through step-by-step continuous learning, the sensors complete the perception and comprehensive modeling of the environment, and trans…
Figure 6
Figure 6. Figure 6: The main tasks of the action module are divided into motion control and feedback adjustment. For motion control (a) Fig. [114] flexible sensors make the intelligent body’s movements more natural and flexible with the help of materials such as (b) Fig. [115] shape memor…
Figure 7
Figure 7. Figure 7: The feedback module [132] is mainly divided into three main categories: perception feedback, decision-making feedback, and action feedback. Perception feedback dynam￾ically adjusts the perception process by collecting external sensor data in real time. Decision feedbac…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

141 extracted references · 63 canonical work pages

  1. [28]

    Key- points into the future: Self-supervised correspondence in model- based reinforcement learning

    Lucas Manuelli, Yunzhu Li, Pete Florence, and Russ Tedrake. Key- points into the future: Self-supervised correspondence in model- based reinforcement learning. arXiv preprint arXiv:2009.05085, 2020

    arXiv 2009

  2. [1]

    Towards artificial generalintelligence(agi)intheinternetofthings(iot):Opportunities and challenges.arXiv preprint arXiv:2309.07438, 2023

    FeiDou,JinYe,GengYuan,QinLu,WeiNiu,HaijianSun,LeGuan, Guoyu Lu, Gengchen Mai, Ninghao Liu, et al. Towards artificial generalintelligence(agi)intheinternetofthings(iot):Opportunities and challenges.arXiv preprint arXiv:2309.07438, 2023

    arXiv 2023

  3. [2]

    Frommachinelearningtorobotics:Challengesandopportunitiesfor embodied intelligence.arXiv preprint arXiv:2110.15245, 2021

    N.Roy,I.Posner,T.Barfoot,P.Beaudoin,Y.Bengio,J.Bohg,etal. Frommachinelearningtorobotics:Challengesandopportunitiesfor embodied intelligence.arXiv preprint arXiv:2110.15245, 2021

    arXiv 2021

  4. [3]

    A path toward explainable ai and autonomous adaptiveintelligence:deeplearning,adaptiveresonance,andmodels of perception, emotion, and action

    Stephen Grossberg. A path toward explainable ai and autonomous adaptiveintelligence:deeplearning,adaptiveresonance,andmodels of perception, emotion, and action. Frontiers in neurorobotics, 14:36, 2020

    2020

  5. [4]

    Embodied intelligence in soft robotics through hardwaremultifunctionality

    Matteo Cianchetti. Embodied intelligence in soft robotics through hardwaremultifunctionality. FrontiersinRoboticsandAI ,8:724056, 2021

    2021

  6. [5]

    Recent advances and trends in multimodal deep learning: A review.arXiv preprint arXiv:2105.11087, 2021

    Jabeen Summaira, Xi Li, Amin Muhammad Shoib, Songyuan Li, and Jabbar Abdul. Recent advances and trends in multimodal deep learning: A review.arXiv preprint arXiv:2105.11087, 2021

    arXiv 2021

  7. [6]

    Embodiedun- derstanding of driving scenarios.arXiv preprint arXiv:2403.04593, 2024

    Y.Zhou,L.Huang,Q.Bu,J.Zeng,T.Li,H.Qiu,etal. Embodiedun- derstanding of driving scenarios.arXiv preprint arXiv:2403.04593, 2024

    arXiv 2024

  8. [7]

    Y. Liu, W. Chen, Y. Bai, J. Luo, X. Song, K. Jiang, et al. Aligning cyber space with physical world: A comprehensive survey on em- bodied ai.arXiv preprint arXiv:2407.06886, 2024

    arXiv 2024

Show all 141 references
  1. [8]

    Ai embodiment through 6g: Shaping the future of agi.IEEE Wireless Communications, 2024

    Lina Bariah and Mérouane Debbah. Ai embodiment through 6g: Shaping the future of agi.IEEE Wireless Communications, 2024

    2024

  2. [9]

    A. M. Turing. Computing machinery and intelligence. Mind, 59(236):433, 1950

    1950

  3. [10]

    Artificial human intelligence: The role of hu- mans in the development of next generation ai

    Suayb S Arslan. Artificial human intelligence: The role of hu- mans in the development of next generation ai. arXiv preprint arXiv:2409.16001, 2024

    2024

  4. [11]

    MIT press, 2008

    Dario Floreano and Claudio Mattiussi.Bio-inspired artificial intel- ligence: theories, methods, and technologies. MIT press, 2008

    2008

  5. [12]

    Adams, I

    S. Adams, I. Arel, J. Bach, R. Coop, R. Furlan, B. Goertzel, et al. Mappingthelandscapeofhuman-levelartificialgeneralintelligence. AI Magazine, 33(1):25–42, 2012

    2012

  6. [13]

    Silver, S

    D. Silver, S. Singh, D. Precup, and R. S. Sutton. Reward is enough. Artificial Intelligence, 299:103535, 2021

    2021

  7. [14]

    generalproblemsolver

    S.C.Garvey. The“generalproblemsolver”doesnotexist:Mortimer taube and the art of ai criticism.IEEE Annals of the History of Computing, 43(1):60–73, 2021

    2021

  8. [15]

    Moto-Oka

    T. Moto-Oka. Overview to the fifth generation computer system project. InProceedingsofthe10thAnnualInternationalSymposium on Computer Architecture, pages 417–422, 1983

    1983

  9. [16]

    Roland and P

    A. Roland and P. Shiman.Strategic Computing: DARPA and the Quest for Machine Intelligence, 1983-1993. MIT Press, 2002

    1983

  10. [17]

    Goertzel

    B. Goertzel. Artificial general intelligence: Concept, state of the art, and future prospects.Journal of Artificial General Intelligence, 5(1):1, 2014

    2014

  11. [18]

    T. J. Sejnowski. The unreasonable effectiveness of deep learning in artificial intelligence. Proceedings of the National Academy of Sciences, 117(48):30033–30038, 2020

    2020

  12. [19]

    Openagi: When llm meets domain experts.Advances in Neural Information Processing Systems, 36, 2024

    Y.Ge,W.Hua,K.Mei,J.Tan,S.Xu,Z.Li,andY.Zhang. Openagi: When llm meets domain experts.Advances in Neural Information Processing Systems, 36, 2024

    2024

  13. [20]

    Pfeifer and F

    R. Pfeifer and F. Iida. Embodied artificial intelligence: Trends and challenges. Lecture Notes in Computer Science, pages 1–26, 2004. : Preprint submitted to Elsevier Page 16 of 19

    2004

  14. [21]

    R. A. Brooks. Intelligence without representation. Artificial Intelligence, 47(1-3):139–159, 1991

    1991

  15. [22]

    Pfeifer and C

    R. Pfeifer and C. Scheier.Understanding Intelligence. MIT Press, 2001

    2001

  16. [23]

    L. B. Smith. Cognition as a dynamic system: Principles from embodiment. Developmental Review, 25(3-4):278–298, 2005

    2005

  17. [24]

    Embodied cognition and learning environment design

    John B Black, Ayelet Segal, Jonathan Vitale, and Cameron L Fadjo. Embodied cognition and learning environment design. In Theoretical foundations of learning environments, pages 198–223. Routledge, 2012

    2012

  18. [25]

    Environ- mental cognition

    Reginald G Golledge, Gary T Moore, Ronald Briggs, Martin T Cadwallader, Ann S Devlin, David L George, Georgia Zannaras, Aleira Kreimer, Alfred J Nigl, Harold D Fishbein, et al. Environ- mental cognition. In Environmental design research, pages 182–

  19. [26]

    Nikoleta Manakitsa, George S Maraslidis, Lazaros Moysis, and George F Fragulis. A review of machine learning and deep learn- ing for object detection, semantic segmentation, and human action recognition in machine and robotic vision.Technologies, 12(2):15, 2024

    2024

  20. [27]

    In-sensor multisensory integrative perception.Available at SSRN 5128520

    Tianrun Li, Zhimiao Yan, Yinghua Chen, and Ting Tan. In-sensor multisensory integrative perception.Available at SSRN 5128520

  21. [29]

    M. R. Morris, J. Sohl-Dickstein, N. Fiedel, T. Warkentin, A. Dafoe, A. Faust, et al. Levels of agi: Operationalizing progress on the path to agi.arXiv preprint arXiv:2311.02462, 2023

    2023

  22. [30]

    H. M. Hegde. Autonomous Path Traversal and Object Avoidance in Cars-AirSim Simulation. PhD thesis, California State University, Northridge, 2021

    2021

  23. [31]

    Nogueira

    L. Nogueira. Comparative analysis between gazebo and v-rep robotic simulators.Seminario Interno de Cognicao Artificial-SICA, 2014(5):2, 2014

    2014

  24. [32]

    F. Xia, W. B. Shen, C. Li, P. Kasimbeg, M. E. Tchapmi, A. Toshev, et al. Interactive gibson benchmark (igibson 0.5): A benchmark for interactive navigation in cluttered environments.arXiv preprint arXiv:2005.04307, 2020

    2005 arXiv

  25. [33]

    Coumans and Y

    E. Coumans and Y. Bai. Pybullet quickstart guide. 2021

    2021

  26. [34]

    Fernandez-Chaves, J

    D. Fernandez-Chaves, J. R. Ruiz-Sarmiento, A. Jaenal, N. Petkov, and J. Gonzalez-Jimenez. Robot@virtualhome, an ecosystem of virtualenvironmentsandtoolsforrealisticindoorroboticsimulation. Expert Systems with Applications, 208:117970, 2022

    2022

  27. [35]

    Designandimplementationofa simulated robot using webots software

    V.GanapathyandL.W.L.Dennis. Designandimplementationofa simulated robot using webots software. InProceedings of the Inter- nationalConference onControl,Instrumentationand Mechatronics Engineering, pages 28–29, 2007

    2007

  28. [36]

    Pybullet quickstart guide.ed: Py- Bullet Quickstart Guide

    Erwin Coumans and Yunfei Bai. Pybullet quickstart guide.ed: Py- Bullet Quickstart Guide. https://docs. google. com/document/u/1/d, 2021

    2021

  29. [37]

    Shigemi, A

    S. Shigemi, A. Goswami, and P. Vadakkepat. Asimo and humanoid robot research at honda.Humanoid Robotics: A Reference, 55:90, 2018

    2018

  30. [38]

    H. Liu, D. Guo, F. Sun, W. Yang, S. Furber, and T. Sun. Embodied tactile perception and learning.Brain Science Advances, 6(2):132– 158, 2020

    2020

  31. [39]

    Y. Qin, E. Zhou, Q. Liu, Z. Yin, L. Sheng, R. Zhang, et al. Mp5: Amulti-modalopen-endedembodiedsysteminminecraftviaactive perception. In2024IEEE/CVFConferenceonComputerVisionand Pattern Recognition (CVPR), pages 16307–16316, 2024

    2024

  32. [40]

    Surveyondeepmulti-modaldataanalytics:Collaboration, rivalry, and fusion.ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 17(1s):1–25, 2021

    Y.Wang. Surveyondeepmulti-modaldataanalytics:Collaboration, rivalry, and fusion.ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 17(1s):1–25, 2021

    2021

  33. [41]

    Winkler and B

    T. Winkler and B. Rinner. Security and privacy protection in visual sensor networks: A survey. ACM Computing Surveys (CSUR), 47(1):1–42, 2014

    2014

  34. [42]

    L. Zou, C. Ge, Z. J. Wang, E. Cretu, and X. Li. Novel tactile sensor technology and smart tactile sensing systems: A review.Sensors, 17(11):2653, 2017

    2017

  35. [43]

    H. Guo, X. Pu, J. Chen, Y. Meng, M. H. Yeh, G. Liu, et al. A highlysensitive,self-poweredtriboelectricauditorysensorforsocial robotics and hearing aids.Science Robotics, 3(20):eaat2516, 2018

    2018

  36. [44]

    Shorten and T

    C. Shorten and T. M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data, 6(1):1–48, 2019

    2019

  37. [45]

    A. F. Elaksher and J. S. Bethel. Reconstructing 3d buildings from lidar data.International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 34(3/A):102–107, 2002

    2002

  38. [46]

    Big data preprocessing: Methods and prospects

    S.García,S.Ramírez-Gallego,J.Luengo,J.M.Benítez,andF.Her- rera. Big data preprocessing: Methods and prospects. Big Data Analytics, 1:1–22, 2016

    2016

  39. [47]

    J. Wu. Introduction to convolutional neural networks.National Key Lab for Novel Software Technology, 2017

    2017

  40. [48]

    Lindemann, T

    B. Lindemann, T. Müller, H. Vietz, N. Jazdi, and M. Weyrich. A survey on long short-term memory networks for time series prediction. Procedia CIRP, 99:650–655, 2021

    2021

  41. [49]

    J. Luo, W. Wang, and H. Qi. Spatio-temporal feature extraction and representation for rgb-d human action recognition. Pattern Recognition Letters, 50:139–148, 2014

    2014

  42. [50]

    Huang, B

    K. Huang, B. Shi, X. Li, X. Li, S. Huang, and Y. Li. Multi-modal sensor fusion for auto driving perception: A survey.arXiv preprint arXiv:2202.02703, 2022

    2022 arXiv

  43. [51]

    Evaluating ensemble learning methods for multi-modal emotion recognition using sensor data fusion.Sensors, 22(15):5611, 2022

    E.M.Younis,S.M.Zaki,E.Kanjo,andE.H.Houssein. Evaluating ensemble learning methods for multi-modal emotion recognition using sensor data fusion.Sensors, 22(15):5611, 2022

    2022

  44. [52]

    Algarni, et al

    N.A.Almujally,A.A.Rafique,N.AlMudawi,A.Alazeb,M.Alon- azi, A. Algarni, et al. Multi-modal remote perception learning for object sensory data.Frontiers in Neurorobotics, 18:1427786, 2024

    2024

  45. [53]

    Simanek, V

    J. Simanek, V. Kubelka, and M. Reinstein. Improving multi-modal data fusion by anomaly detection.Autonomous Robots, 39(2):139– 154, 2015

    2015

  46. [54]

    Onrobustnessofmulti-modal fusion—robotics perspective.Electronics, 9(7):1152, 2020

    M.Bednarek,P.Kicki,andK.Walas. Onrobustnessofmulti-modal fusion—robotics perspective.Electronics, 9(7):1152, 2020

    2020

  47. [55]

    Multi-sensordata fusionmethodbasedonself-attentionmechanism

    X.Lin,S.Chao,D.Yan,L.Guo,Y.Liu,andL.Li. Multi-sensordata fusionmethodbasedonself-attentionmechanism. AppliedSciences, 13(21):11992, 2023

    2023

  48. [56]

    Roheda, H

    S. Roheda, H. Krim, and B. S. Riggan. Robust multi-modal sensor fusion: An adversarial approach. IEEE Sensors Journal, 21(2):1885–1896, 2020

    2020

  49. [57]

    Nadon, A

    F. Nadon, A. J. Valencia, and P. Payeur. Multi-modal sensing and robotic manipulation of non-rigid objects: A survey. Robotics, 7(4):74, 2018

    2018

  50. [58]

    Noceti, B

    N. Noceti, B. Caputo, C. Castellini, L. Baldassarre, A. Barla, L. Rosasco, et al. Towards a theoretical framework for learning multi-modal patterns for embodied agents. InInternational Con- ference on Image Analysis and Processing, pages 239–248, 2009

    2009

  51. [59]

    Z. Jia, J. Wang, and R. Jin. Grnet: A graph reasoning network for enhanced multi-modal learning in scene text recognition.The Computer Journal, bxae085, 2024

    2024

  52. [60]

    J. B. de la Cita.Multimodal Perception for Autonomous Driving. PhD thesis, Universidad Carlos III de Madrid, 2022

    2022

  53. [61]

    Fritsch, M

    J. Fritsch, M. Kleinehagenbrock, S. Lang, T. Plötz, G. A. Fink, and G. Sagerer. Multi-modal anchoring for human–robot interaction. Robotics and Autonomous Systems, 43(2-3):133–147, 2003

    2003

  54. [62]

    Q. Tang, J. Liang, and F. Zhu. A comparative review on multi- modal sensors fusion based on deep learning.Signal Processing, page 109165, 2023

    2023

  55. [63]

    Planninganddecision- making for autonomous vehicles

    W.Schwarting,J.Alonso-Mora,andD.Rus. Planninganddecision- making for autonomous vehicles. Annual Review of Control, Robotics, and Autonomous Systems, 1(1):187–210, 2018

    2018

  56. [64]

    J. Singh. Advancements in ai-driven autonomous robotics: Lever- agingdeeplearningforreal-timedecisionmakingandobjectrecog- nition. Journal of Artificial Intelligence Research and Applications, 3(1):657–697, 2023. : Preprint submitted to Elsevier Page 17 of 19

    2023

  57. [65]

    Tsarouchi, S

    P. Tsarouchi, S. Makris, and G. Chryssolouris. Human–robot interaction review and challenges on task planning and program- ming.InternationalJournalofComputerIntegratedManufacturing , 29(8):916–931, 2016

    2016

  58. [66]

    Hierarchicalreinforcementlearningwiththemaxq value function decomposition

    T.G.Dietterich. Hierarchicalreinforcementlearningwiththemaxq value function decomposition. Journal of Artificial Intelligence Research, 13:227–303, 2000

    2000

  59. [67]

    O. Michel. Cyberbotics ltd. webots™: Professional mobile robot simulation. International Journal of Advanced Robotic Systems, 1(1):5, 2004

    2004

  60. [68]

    L. Hohl, R. Tellez, O. Michel, and A. J. Ijspeert. Aibo and webots: Simulation,wirelessremotecontrolandcontrollertransfer. Robotics and Autonomous Systems, 54(6):472–485, 2006

    2006

  61. [69]

    Robottaskplanningusingsemanticmaps

    C.Galindo,J.A.Fernández-Madrigal,J.González,andA.Saffiotti. Robottaskplanningusingsemanticmaps. RoboticsandAutonomous Systems, 56(11):955–966, 2008

    2008

  62. [70]

    To- wardhuman-awarerobottaskplanning

    R.Alami,A.Clodic,V.Montreuil,E.A.Sisbot,andR.Chatila. To- wardhuman-awarerobottaskplanning. In AAAISpringSymposium: ToBoldlyGoWhereNoHuman-RobotTeamHasGoneBefore ,pages 39–46, 2006

    2006

  63. [71]

    Paxton, Y

    C. Paxton, Y. Barnoy, K. Katyal, R. Arora, and G. D. Hager. Visual robot task planning. In2019 International Conference on Robotics and Automation (ICRA), pages 8832–8838, 2019

    2019

  64. [72]

    Gupta, S

    A. Gupta, S. Savarese, S. Ganguli, and L. Fei-Fei. Embodied intelligence via learning and evolution.Nature Communications, 12(1):5721, 2021

    2021

  65. [73]

    Evolution of embodied intelligence

    D.Floreano,F.Mondada,A.Perez-Uribe,andD.Roggen. Evolution of embodied intelligence. In Embodied Artificial Intelligence: International Seminar, Dagstuhl Castle, Germany, July 7-11, 2003. Revised Papers, pages 293–311, 2004

    2003

  66. [74]

    InConference on Robot Learning, pages 1–10

    Junning Huang, Sirui Xie, Jiankai Sun, Qiurui Ma, Chunxiao Liu, DahuaLin,andBoleiZhou.Learningadecisionmodulebyimitating driver’s control behaviors. InConference on Robot Learning, pages 1–10. PMLR, 2021

    2021

  67. [75]

    Sadhu, T

    A. Sadhu, T. Gupta, M. Yatskar, R. Nevatia, and A. Kembhavi. Visual semantic role labeling for video understanding. InProceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5589–5600, 2021

    2021

  68. [76]

    H. Guo, F. Wu, Y. Qin, R. Li, K. Li, and K. Li. Recent trends in task and motion planning for robotics: A survey.ACM Computing Surveys, 55(13s):1–36, 2023

    2023

  69. [77]

    Policygradi- entmethodsforreinforcementlearningwithfunctionapproximation

    R.S.Sutton,D.McAllester,S.Singh,andY.Mansour. Policygradi- entmethodsforreinforcementlearningwithfunctionapproximation. In Advances in Neural Information Processing Systems, volume 12, 1999

    1999

  70. [78]

    Ho and S

    J. Ho and S. Ermon. Generative adversarial imitation learning. In Advances in Neural Information Processing Systems, volume 29, 2016

    2016

  71. [79]

    Hospedales, A

    T. Hospedales, A. Antoniou, P. Micaelli, and A. Storkey. Meta- learninginneuralnetworks:Asurvey. IEEETransactionsonPattern Analysis and Machine Intelligence, 44(9):5149–5169, 2021

    2021

  72. [80]

    Salimans, J

    T. Salimans, J. Ho, X. Chen, S. Sidor, and I. Sutskever. Evolution strategies as a scalable alternative to reinforcement learning.arXiv preprint arXiv:1703.03864, 2017

    2017 arXiv

  73. [81]

    X. Hong, Y. Lan, L. Pang, J. Guo, and X. Cheng. Visual reasoning: Fromstatetotransformation. IEEETransactionsonPatternAnalysis and Machine Intelligence, 45(9):11352–11364, 2023

    2023

  74. [82]

    Driess, J

    D. Driess, J. S. Ha, and M. Toussaint. Deep visual reasoning: Learning to predict action sequences for task and motion planning fromaninitialsceneimage. arXivpreprintarXiv:2006.05398 ,2020

    2006 arXiv

  75. [83]

    X. Li, F. Zhang, H. Diao, Y. Wang, X. Wang, and L. Y. Duan. Densefusion-1m: Merging vision experts for comprehensive multi- modal perception.arXiv preprint arXiv:2407.08303, 2024

    2024 arXiv

  76. [84]

    H. Cai, Y. Wang, L. Liu, S. Zhu, and M. Chen. Dagnn: Deep autoencoder-based graph neural network for local anomaly detec- tion. In 2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE), pages 988–994, 2024

    2024

  77. [85]

    T. Zhou, T. Shi, H. Gao, and W. Rao. Learning to optimize state es- timation in multi-agent reinforcement learning-based collaborative detection. IEEE Transactions on Mobile Computing, 2024

    2024

  78. [86]

    L. Li, T. Zhou, W. Wang, J. Li, and Y. Yang. Deep hierarchical se- mantic segmentation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1246–1257, 2022

    2022

  79. [87]

    L. Wang, X. Wu, Y. Zhang, X. Zhang, L. Xu, Z. Wu, and A. Fei. Deepadain-net: Deep adaptive device-edge collaborative inference for augmented reality. IEEE Journal of Selected Topics in Signal Processing, 2023

    2023

  80. [88]

    Okubo and M

    T. Okubo and M. Takahashi. Multi-agent action graph based task allocation and path planning considering changes in environment. IEEE Access, 11:21160–21175, 2023

    2023

  81. [89]

    Multi-usvtaskplanning method based on improved deep reinforcement learning

    J.Zhang,J.Ren,Y.Cui,D.Fu,andJ.Cong. Multi-usvtaskplanning method based on improved deep reinforcement learning. IEEE Internet of Things Journal, 2024

    2024

  82. [90]

    Skilld- iffuser: Interpretable hierarchical planning via skill abstractions in diffusion-based task execution

    Z.Liang,Y.Mu,H.Ma,M.Tomizuka,M.Ding,andP.Luo. Skilld- iffuser: Interpretable hierarchical planning via skill abstractions in diffusion-based task execution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16467–16476, 2024

    2024

  83. [91]

    M. Klar, P. Ruediger, M. Schuermann, G. T. Gören, M. Glatt, B. Ravani, and J. C. Aurich. Explainable generative design in man- ufacturingforreinforcementlearningbasedfactorylayoutplanning. Journal of Manufacturing Systems, 72:74–92, 2024

    2024

  84. [92]

    J. Sun, Q. Zhang, Y. Duan, X. Jiang, C. Cheng, and R. Xu. Prompt, plan, perform: Llm-based humanoid control via quantized imitation learning. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 16236–16242, 2024

    2024

  85. [93]

    Liang, R

    Z. Liang, R. Yang, J. Wang, L. Liu, X. Ma, and Z. Zhu. Dynamic constrained evolutionary optimization based on deep q-network. Expert Systems with Applications, 249:123592, 2024

    2024

  86. [94]

    Afew-shot diseasediagnosisdecisionmakingmodelbasedonmeta-learningfor general practice

    Q.Liu,Y.Tian,T.Zhou,K.Lyu,R.Xin,Y.Shang,etal. Afew-shot diseasediagnosisdecisionmakingmodelbasedonmeta-learningfor general practice. Artificial Intelligence in Medicine, 147:102718, 2024

    2024

  87. [95]

    arXiv preprint arXiv:2402.08848, 2024

    J.Ren,G.Swamy,Z.S.Wu,J.A.Bagnell,andS.Choudhury.Hybrid inverse reinforcement learning. arXiv preprint arXiv:2402.08848, 2024

    2024 arXiv

  88. [96]

    J. Liu, X. Qi, P. Hang, and J. Sun. Enhancing social decision- making of autonomous vehicles: A mixed-strategy game approach with interaction orientation identification. IEEE Transactions on Vehicular Technology, 2024

    2024

  89. [97]

    Jana, and N

    G.S.Sahoo,R.H.J.Rani,M.K.Goyal,V.A.Mohammed,D.L.F. Jana, and N. N. Wasatkar. Leveraging adaptive algorithms for real- time data analysis. In2024 15th International Conference on Com- puting Communication and Networking Technologies (ICCCNT), pages 1–6, 2024

    2024

  90. [98]

    K. Dong, Y. Luo, Y. Wang, Y. Liu, C. Qu, Q. Zhang, et al. Dyna- style model-based reinforcement learning with model-free policy optimization. Knowledge-Based Systems, 287:111428, 2024

    2024

  91. [99]

    J. Shuford. Deep reinforcement learning unleashing the power of ai in decision-making. Journal of Artificial Intelligence General Science (JAIGS) ISSN: 3006-4023, 1(1), 2024

    2024

  92. [100]

    Rimon, T

    Z. Rimon, T. Jurgenson, O. Krupnik, G. Adler, and A. Tamar. Mamba:Aneffectiveworldmodelapproachformeta-reinforcement learning. arXiv preprint arXiv:2403.09859, 2024

    2024 arXiv

  93. [101]

    A. Wachi. Failure-scenario maker for rule-based agent using multi- agent adversarial reinforcement learning and its application to au- tonomous driving.arXiv preprint arXiv:1903.10654, 2019

    1903 arXiv

  94. [102]

    Y. Wu, Y. Chen, L. Wang, Y. Ye, Z. Liu, Y. Guo, and Y. Fu. Large scale incremental learning. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 374–382, 2019

    2019

  95. [103]

    Astudy offorward-forwardalgorithm for self-supervised learning.arXiv preprint arXiv:2309.11955, 2023

    J.Brenig andR.Timofte. Astudy offorward-forwardalgorithm for self-supervised learning.arXiv preprint arXiv:2309.11955, 2023. : Preprint submitted to Elsevier Page 18 of 19

    2023 arXiv

  96. [104]

    Z. Wang, Y. Wu, and Q. Niu. Multi-sensor fusion in automated driving: A survey.IEEE Access, 8:2847–2868, 2019

    2019

  97. [105]

    Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age.IEEE Transactions on Robotics, 32(6):1309–1332, 2016

    C.Cadena,L.Carlone,H.Carrillo,Y.Latif,D.Scaramuzza,J.Neira, et al. Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age.IEEE Transactions on Robotics, 32(6):1309–1332, 2016

    2016

  98. [106]

    T. Luo, B. Subagdja, D. Wang, and A. H. Tan. Multi-agent collabo- rativeexplorationthroughgraph-baseddeepreinforcementlearning. In2019IEEEInternationalConferenceonAgents(ICA) ,pages2–7. IEEE, October 2019

    2019

  99. [107]

    Multi-usvcooperativechasing strategybasedonobstaclesassistanceanddeepreinforcementlearn- ing

    W.Gan,X.Qu,D.Song,andP.Yao. Multi-usvcooperativechasing strategybasedonobstaclesassistanceanddeepreinforcementlearn- ing. IEEE Transactions on Automation Science and Engineering, 2023

    2023

  100. [108]

    Cooperative and competitive multi-agent systems: From optimiza- tion to games.IEEE/CAA Journal of Automatica Sinica, 9(5):763– 783, 2022

    J.Wang,Y.Hong,J.Wang,J.Xu,Y.Tang,Q.L.Han,andJ.Kurths. Cooperative and competitive multi-agent systems: From optimiza- tion to games.IEEE/CAA Journal of Automatica Sinica, 9(5):763– 783, 2022

    2022

  101. [109]

    Atheoreticalanalysisofdeep q-learning

    J.Fan,Z.Wang,Y.Xie,andZ.Yang. Atheoreticalanalysisofdeep q-learning. In Learning for dynamics and control, pages 486–489. PMLR, July 2020

    2020

  102. [110]

    Fast context adaptation via meta-learning

    L.Zintgraf,K.Shiarli,V.Kurin,K.Hofmann,andS.Whiteson. Fast context adaptation via meta-learning. InInternational Conference on Machine Learning, pages 7693–7702. PMLR, May 2019

    2019

  103. [111]

    V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015

    2015

  104. [112]

    learning-to-communicate

    H. Mao, Z. Gong, Y. Ni, and Z. Xiao. Accnet: Actor-coordinator- criticnetfor"learning-to-communicate"withdeepmulti-agentrein- forcement learning.arXiv preprint arXiv:1706.03235, 2017

    2017 arXiv

  105. [113]

    L.JingandY.Tian.Self-supervisedvisualfeaturelearningwithdeep neural networks: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(11):4037–4058, 2020

    2020

  106. [114]

    J. C. Yeo, H. K. Yap, W. Xi, Z. Wang, C. H. Yeow, and C. T. Lim. Flexible and stretchable strain sensing actuator for wear- able soft robotic applications. Advanced Materials Technologies, 1(3):1600018, 2016

    2016

  107. [115]

    Areviewofshape memoryalloyresearch,applicationsandopportunities

    J.M.Jani,M.Leary,A.Subic,andM.A.Gibson. Areviewofshape memoryalloyresearch,applicationsandopportunities. Materials& Design (1980-2015), 56:1078–1113, 2014

    1980

  108. [116]

    Bar-Cohen

    Y. Bar-Cohen. Electroactive polymers: current capabilities and challenges. In Smart Structures and Materials 2002: Electroactive PolymerActuatorsand Devices(EAPAD) ,volume4695, pages1–7. SPIE, July 2002

    2002

  109. [117]

    L. Chen, Q. Hu, H. Zhang, B. Tong, X. Shi, C. Jiang, and L. Sun. Researchonunderwatermotionmodelingandclosed-loopcontrolof bionicundulatingfinrobot. OceanEngineering,299:117400,2024

    2024

  110. [118]

    B. M. Wilamowski. Neural network architectures and learning algorithms. IEEE Industrial Electronics Magazine, 3(4):56–63, 2009

    2009

  111. [119]

    Bhatti, H

    G. Bhatti, H. Mohan, and R. R. Singh. Towards the future of smart electric vehicles: Digital twin technology. Renewable and Sustainable Energy Reviews, 141:110801, 2021

    2021

  112. [120]

    Renewable and sustainable energy reviews

    Chang-Hun Lee. Renewable and sustainable energy reviews. Coastal and Ocean, 5(2):62–70, 2012

    2012

  113. [121]

    F. C. Gu, H. C. Chang, Y. M. Hsueh, C. C. Kuo, and B. R. Chen. Developmentofahigh-speeddataacquisitioncardforpartial discharge measurement.IEEE Access, 7:140312–140318, 2019

    2019

  114. [122]

    Securityandprivacyprotec- tion in visual sensor networks: A survey.ACM Computing Surveys (CSUR), 47(1):1–42, 2014

    ThomasWinklerandBernhardRinner. Securityandprivacyprotec- tion in visual sensor networks: A survey.ACM Computing Surveys (CSUR), 47(1):1–42, 2014

    2014

  115. [123]

    George Thuruthel, Y

    T. George Thuruthel, Y. Ansari, E. Falotico, and C. Laschi. Control strategies for soft robotic manipulators: A survey.Soft Robotics, 5(2):149–163, 2018

    2018

  116. [124]

    Castelli, S

    F. Castelli, S. Michieletto, S. Ghidoni, and E. Pagello. A machine learning-based visual servoing approach for fast robot control in in- dustrialsetting. InternationalJournalofAdvancedRoboticSystems , 14(6):1729881417738884, 2017

    2017

  117. [125]

    G. S. Dordevic, M. Rasic, and R. Shadmehr. Parametric models for motion planning and control in biomimetic robotics. IEEE Transactions on Robotics, 21(1):80–92, 2005

    2005

  118. [126]

    S. Duan, Q. Shi, and J. Wu. Multimodal sensors and ml-based data fusion for advanced robots. Advanced Intelligent Systems, 4(12):2200213, 2022

    2022

  119. [127]

    Zhang, C

    J. Zhang, C. Song, Y. Hu, and B. Yu. Improving robustness of roboticgraspingbyfusingmulti-sensor. In 2012IEEEInternational Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pages 126–131. IEEE, September 2012

    2012

  120. [128]

    Neuralnetworkmapping of industrial robots’ task times for real-time process optimization

    P.Righettini,R.Strada,andF.Cortinovis. Neuralnetworkmapping of industrial robots’ task times for real-time process optimization. Robotics, 12(5):143, 2023

    2023

  121. [129]

    D. M. Botín-Sanabria, A. S. Mihaita, R. E. Peimbert-García, M. A. Ramírez-Moreno, R. A. Ramírez-Mendoza, and J. D. J. Lozoya- Santos. Digital twin technology challenges and applications: A comprehensive review.Remote Sensing, 14(6):1335, 2022

    2022

  122. [130]

    L. Ren, J. Dong, S. Liu, L. Zhang, and L. Wang. Embodied intelli- gence toward future smart manufacturing in the era of ai foundation model. IEEE/ASME Transactions on Mechatronics, 2024

    2024

  123. [131]

    Yamakawa, Y

    Y. Yamakawa, Y. Matsui, and M. Ishikawa. Development of a real-time human-robot collaborative system based on 1 khz visual feedback control and its application to a peg-in-hole task.Sensors, 21(2):663, 2021

    2021

  124. [132]

    Vasilopoulos, G

    V. Vasilopoulos, G. Pavlakos, S. L. Bowman, J. D. Caporale, K.Daniilidis,G.J.Pappas,andD.E.Koditschek. Reactivesemantic planning in unexplored semantic environments using deep percep- tual feedback. IEEE Robotics and Automation Letters, 5(3):4455– 4462, 2020

    2020

  125. [133]

    R. C. Luo and M. G. Kay. Multisensor integration and fusion in intelligent systems. IEEE Transactions on Systems, Man, and Cybernetics, 19(5):901–931, 1989

    1989

  126. [134]

    Asensorfordynamictactile informationwithapplicationsinhuman–robotinteractionandobject exploration

    P.A.Schmidt,E.Maël,andR.P.Würtz. Asensorfordynamictactile informationwithapplicationsinhuman–robotinteractionandobject exploration. RoboticsandAutonomousSystems ,54(12):1005–1014, 2006

    2006

  127. [135]

    L. A. Dennis, M. Fisher, N. K. Lincoln, A. Lisitsa, and S. M. Veres. Practicalverificationofdecision-makinginagent-basedautonomous systems. Automated Software Engineering, 23:305–359, 2016

    2016

  128. [136]

    Integratinghumanandrobot decision-making dynamics with feedback: Models and convergence analysis

    M.Cao,A.Stewart,andN.E.Leonard. Integratinghumanandrobot decision-making dynamics with feedback: Models and convergence analysis. In 2008 47th IEEE Conference on Decision and Control, pages 1127–1132. IEEE, December 2008

    2008

  129. [137]

    Synthesisforrobots: Guarantees and feedback for robot behavior

    H.Kress-Gazit,M.Lahijanian,andV.Raman. Synthesisforrobots: Guarantees and feedback for robot behavior. Annual Review of Control, Robotics, and Autonomous Systems, 1(1):211–236, 2018

    2018

  130. [138]

    Merckaert, B

    K. Merckaert, B. Convens, C. J. Wu, A. Roncone, M. M. Nicotra, and B. Vanderborght. Real-time motion control of robotic manip- ulators for safe human–robot coexistence.Robotics and Computer- Integrated Manufacturing, 73:102223, 2022

    2022

  131. [139]

    Ferrucci and S

    F. Ferrucci and S. Bock. Real-time control of express pickup and delivery processes in a dynamic environment.Transportation Research Part B: Methodological, 63:1–14, 2014

    2014

  132. [140]

    Gheibi, D

    O. Gheibi, D. Weyns, and F. Quin. Applying machine learning in self-adaptive systems: A systematic literature review.ACM Trans- actions on Autonomous and Adaptive Systems (TAAS), 15(3):1–37, 2021

    2021

  133. [141]

    Huang, C

    Z. Huang, C. Lv, Y. Xing, and J. Wu. Multi-modal sensor fusion- based deep neural network for end-to-end autonomous driving with scene understanding.IEEE Sensors Journal, 21(10):11781–11790, 2020. : Preprint submitted to Elsevier Page 19 of 19

    2020

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.