Pith. sign in

REVIEW 4 major objections 7 minor 41 references

GAMORA: A Gesture Articulated Meta Operative Robotic Arm for Hazardous Material Handling in Containment-Level Environments

T0 review · 4 major / 7 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read GAMORA is a VR-guided, low-cost robotic arm that the paper claims places hazardous-lab specimens within 2.2 mm and pipettes within 0.2 mL.

desk verdict Plausible low-cost VR teleop integration, but the headline accuracy numbers are unsupported by internally inconsistent, in-sample reporting. read the letter →

arxiv 2506.14513 v1 pith:LGVDTBLG submitted 2025-06-17 cs.RO cs.AIcs.CV

Farha Abdul Wasay , Mohammed Abdul Rahman , Hania Ghouse This is my paper · ORCID
classification cs.ROcs.AIcs.CV
keywords VRteleoperationroboticarmdigitaltwininversekinematicsbiosafetypipettingautomationROSlaboratory
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

The paper proposes GAMORA (Gesture Articulated Meta Operative Robotic Arm), a VR-guided robotic arm built from a 3D-printed 5-degree-of-freedom arm, an Oculus Quest 2 headset, a Jetson Nano, and ROS, and argues that this low-cost combination can perform hazardous lab tasks remotely. The central claim is that after iterative calibration the arm places specimens with a mean positional discrepancy of 2.2 mm, pipettes within 0.2 mL of a 1 mL target, and repeats placements within ±1.2 mm over 50 trials. A sympathetic reader would care because virology and containment laboratories need ways to keep humans away from infectious samples while retaining the dexterity and precision of manual work. The paper's contribution is a complete digital-twin-to-physical pipeline, with training and execution sharing the same interface.

What carries the argument

The carrying mechanism is a digital twin pipeline: a Unity 3D virtual workspace that mirrors the physical arm through ROS over a 5 GHz link, with the Oculus Quest 2 providing gesture input and the Jetson Nano running control computation. For inverse kinematics, the paper uses planar 2-DOF equations, $\cos\theta_2 = (x^2 + y^2 - L_1^2 - L_2^2)/(2L_1L_2)$, $\theta_2 = \operatorname{atan2}(\sin\theta_2, \cos\theta_2)$, and $\theta_1 = \operatorname{atan2}(y, x) - \operatorname{atan2}(L_2\sin\theta_2, L_1 + L_2\cos\theta_2)$, while MoveIt! with RRT, PRM, and KDL solvers generates collision-free trajectories and YOLOv8 supplies object detection. Hardware-in-the-loop testing, with RViz visualization, is the refinement step that turns simulation into the reported physical accuracy.

What would settle it

Use an external motion-capture or laser tracker to record the end-effector position during the same 50-trial specimen-placement protocol, and compare those measurements to the reported mean 2.2 mm discrepancy; if the externally measured error is substantially larger or varies with joint configuration, the central accuracy claim would not hold.

Watch

Extended reading notes

Core claim

On its own terms, the paper's discovery is that an end-to-end gesture-controlled robotic system can close the loop between a Unity virtual environment and a physical 3D-printed arm and reach practical laboratory precision. GAMORA reduced positional discrepancy from 4.0 mm to 2.2 mm, angular misalignment from 8.5° to 2.5° during vial insertion, pipetting deviation from 0.4 mL to 0.2 mL against a 1 mL target, and repeatability error from ±2.8 mm to ±1.2 mm across 50 consecutive cycles, while lowering planning time to 0.5 s and achieving a 90–95% path planning success rate. The paper reports these numbers as evidence that the system is ready for specimen handling, pipetting, and multi-well plate preparation in containment-level environments.

Load-bearing premise

The paper assumes the virtual model of the arm matches the real 3D-printed arm closely enough that commands computed in simulation produce the reported 2.2 mm accuracy, yet it never checks this with an independent outside measurement.

Editorial extensions

If this is right

  • A containment-lab operator can handle specimen vials, pipetting, and multi-well plate preparation from outside the hazard zone with the reported mm-scale accuracy and mL-scale volume error.
  • Since the same VR environment is used for training and execution, operators can rehearse risky gestures without exposure and then run the same motions on the physical arm.
  • The 50-cycle ±1.2 mm repeatability indicates sustained performance for repetitive transfer protocols, not just one-shot accuracy.
  • The reduced planning time (0.5 s) and 90–95% path success make interactive, human-in-the-loop control feasible during live tasks.
  • Lower power use (100 W to 50 W) and reduced CPU/RAM usage point toward longer-duration deployments in sealed containment suites.

Reading between the lines

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

  • Editorial inference: the reported 2.2 mm was measured in pilot trials on one prototype; whether it transfers to other 3D-printed arms or other lab layouts needs independent validation, because joint backlash and 3D-printing tolerances vary.
  • Editorial inference: the planar 2-DOF IK equations cover only two joints, so a full 5-DOF IK solution or a calibration step for the remaining joints is likely needed before the system can reach arbitrary poses in cluttered containment spaces.
  • Editorial inference: a 0.2 mL deviation on a 1 mL pipetting target is suitable for qualitative and coarse quantitative work; for high-accuracy analytical assays a stricter volume-error budget would need to be demonstrated.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 7 minor

Summary. The paper describes GAMORA, a 5-DOF 3D-printed robotic arm teleoperated through an Oculus Quest 2 headset with a Unity/ROS digital twin, targeting specimen handling and pipetting in hazardous laboratory environments. The authors report mean positional discrepancy improving from 4.0 mm to 2.2 mm, repeatability of ±1.2 mm over 50 trials, pipetting deviation within 0.2 mL of a 1 mL target, and reductions in planning time and power consumption. The central claim is that the integrated low-cost VR-guided system achieves mm-scale placement accuracy and mL-scale liquid-handling accuracy in containment-level tasks.

Significance. If substantiated, GAMORA would be a useful low-cost teleoperation platform for biosafety laboratories, combining consumer VR hardware, ROS, MoveIt!, and a 3D-printed arm. The integration of a Unity/ROS digital twin with hardware-in-the-loop testing is a reasonable design direction, and the authors are credited for assembling and testing a multi-component system. However, the paper currently provides no raw per-trial data, no error bars, no measurement protocol, no statistical analysis, and no independent validation of the physical arm's accuracy. The quantitative claims therefore cannot be verified, and the significance of the contribution is not currently assessable.

major comments (4)
  1. [Abstract; VI.A–VI.D] The central quantitative results (2.2 mm positional discrepancy, ±1.2 mm repeatability over 50 trials, 0.2 mL pipetting deviation) are reported without raw per-trial data, error bars, a measurement protocol, or statistical analysis. Figures 8 and 9 are referenced as evidence of improvement and repeatability, but no data plots, axes, or measurement details are provided. Without external ground-truth measurement or at least a clear protocol with per-trial numbers, these values cannot be verified.
  2. [V.B] The accuracy improvements are obtained through iterative tuning: the text states that 'Discrepancies between simulated and physical outcomes were used to iteratively refine both the kinematic model and control algorithms.' No separation between calibration trials and held-out evaluation trials is described, and no independent test set is mentioned. Therefore the reported 2.2 mm, ±1.2 mm, and related values are in-sample, post-tuning results rather than an assessment of the deployed system's accuracy.
  3. [IV.C, Eqs. (1)–(4)] The inverse-kinematics equations presented are for a planar 2-DOF manipulator, yet the system is a 5-DOF arm with servo backlash and link compliance. No kinematic identification, joint-offset calibration, or external measurement (e.g., motion capture or calibrated vision) is reported to validate that the URDF/MoveIt! model represents the physical arm. Since controller commands are generated from this model, the 2.2 mm positional-accuracy claim depends on an unverified digital-to-physical mapping.
  4. [VI.A, VI.B, VI.D, Conclusion] The reported metrics are internally inconsistent. Specimen-handling repeatability is given as ±2.8 mm over 20 cycles in VI.A but as ±1.2 mm over 50 trials in the abstract and VI.D; pipetting deviation is 0.2 mL in VI.B but ±0.1 mL in the Conclusion; and VI.C claims a 0.3 mm placement accuracy for well dispensing that is not reconciled with the 2.2 mm positional figure. These contradictions make the quantitative results unreliable.
minor comments (7)
  1. [II, III] The introduction and literature review claim the system integrates 'reinforcement learning,' but no RL component appears in the Methods, Experiments, or Results; please either implement and evaluate it or remove the claim.
  2. [III.A, V.A, V.C] The hardware setup lists a Ricoh Theta SC2 camera, but the calibration experiments evaluate a 'depth-sensing camera,' and V.C mentions joint encoders and force-torque sensors; these components are not listed in the hardware description, so it is unclear whether they were actually available.
  3. [IV.D] YOLOv8 is used with default pretrained weights, but no detection performance metrics (e.g., mAP, precision/recall) or task-level benefit are reported, so the contribution to 'spatial awareness' is not quantitatively supported.
  4. [VI.E] The power and current reductions (100 W to 50 W, 2 A to 1 A, etc.) are reported without a measurement procedure or circuit description; specify how these values were obtained and under what load conditions.
  5. [References] Several citations do not match the text; for example, [30] is attributed to 'Lundeen et al.' but the listed reference is Wang (2022), and [27] is cited as 'Kuts et al.' but the reference is Singh et al. The bibliography should be corrected and aligned with the text.
  6. [Figures 8 and 9] Figures 8 and 9 are described as showing the improvements and repeatability, but the manuscript does not include the actual plots or sufficient caption/axis detail to interpret them; please include the figures with proper labels.
  7. [VI.B, VI.E] The success rates ('95%', '90–95%') are used without a definition of success, the number of repetitions, or the acceptance criteria for each task; these should be stated explicitly.

Circularity Check

1 steps flagged · score 6.0 of 10

Central 2.2 mm accuracy claim is an in-sample post-calibration residual, not an independent result.

  1. fitted input called prediction [Section V.B (Iterative Testing, Optimization, and System Refinement) and Section VI.A (Specimen Handling and Placing)]
    "Section V.B: 'Discrepancies between simulated and physical outcomes were used to iteratively refine both the kinematic model and control algorithms. Inverse kinematics solvers were tuned to reduce positional error.' Section VI.A: 'Positional Accuracy: Improved from 4.0 mm to 2.2 mm after calibration.'"

    The reported headline metric (2.2 mm mean positional discrepancy) is the same quantity the calibration procedure was explicitly optimizing: V.B says discrepancies were used to refine the model and IK solvers were tuned to reduce positional error, and VI.A reports the 2.2 mm value as an improvement 'after calibration.' No held-out trials or train/test split are described, so the improvement is the residual of the fitting process itself, not an independent validation. Presenting this in-sample post-tuning measurement as the system's achieved accuracy is equivalent to reporting the minimized objective as a prediction.

full rationale

The paper's mathematical content is standard: Eqs. (1)-(4) are textbook planar 2-DOF inverse kinematics, and YOLOv8 with default pretrained weights is an external component, so there is no self-citation chain or ansatz-smuggling. The central circularity is in the evaluation: the headline positional-accuracy and repeatability improvements are reported after the same iterative calibration/tuning loop that was explicitly minimizing those errors (V.B), with no separated training and test trials. The abstract's 2.2 mm figure and the improved metrics in Figure 8 therefore reduce to in-sample fit residuals. Additional internal inconsistencies (repeatability ±2.8 mm over 20 cycles in VI.A vs ±1.2 mm over 50 trials in the abstract/VI.D; pipetting deviation 0.2 mL in VI.B vs ±0.1 mL in the conclusion) undermine the presentation but are consistency issues, not circularity. Because the central claim itself reduces to the calibration objective, the circularity score is 6; the kinematics themselves are not circular.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

No new physical entities or forces are introduced. The hidden inputs are calibration parameters and modeling assumptions: the URDF fidelity, the applicability of 2-DOF IK to a 5-DOF arm, the low-latency link from VR headset to robot, and the independence of the evaluation from calibration.

free parameters (2)
  • Calibration corrections and IK tuning constants = not reported
    Section V-B says IK solvers were tuned to reduce positional error using discrepancies between simulated and physical outcomes; the 2.2 mm and 2.5° results depend on these undisclosed settings. The system's central accuracy claim therefore rests on at least one set of fitted parameters.
  • Dynamic motion adjustment parameters for pipetting = not reported
    Section VI.B says initial pipette alignment error was reduced by dynamic motion adjustments, but the adjustment rule and values are not given, so the 0.2 mL deviation depends on unstated settings.
assumptions (4)
  • domain assumption The URDF kinematic model accurately represents the physical 3D-printed arm, including link lengths, joint limits, and servo behavior.
    Invoked in Section IV-B and IV-C; if the model diverges from the physical arm, simulated poses and reported mm-level accuracy are invalid.
  • ad hoc to paper The planar 2-DOF IK equations (Eq. 1-4) are sufficient to control the 5-DOF arm.
    Section IV-C provides only planar two-link IK for a 5-DOF arm; wrist geometry, extra joint redundancy, and orientation constraints are not described.
  • domain assumption The Oculus Quest 2 controller tracking and the 5 GHz WiFi/Bluetooth connection add negligible latency and drift.
    Section III-A specifies the communication link; no latency, jitter, or drift measurements are reported, yet sub-3 mm accuracy depends on tight real-time coupling.
  • ad hoc to paper The reported metrics come from an independent evaluation protocol, not from the same trials used for calibration.
    Sections V-B and VI.A describe iterative tuning followed by improved results; no held-out trials or cross-validation are mentioned, so independence is assumed.

how reviews work

0 comments
Cite this review

Pith. "Pith review of GAMORA: A Gesture Articulated Meta Operative Robotic Arm for Hazardous Material Handling in Containment-Level Environments." pith.science (2026). https://pith.science/paper/LGVDTBLG

@misc{pith2026250614513,
  author       = {Pith},
  title        = {Pith review of: GAMORA: A Gesture Articulated Meta Operative Robotic Arm for Hazardous Material Handling in Containment-Level Environments},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LGVDTBLG}},
  note         = {Machine review of arXiv:2506.14513}
}
read the original abstract

The convergence of robotics and virtual reality (VR) has enabled safer and more efficient workflows in high-risk laboratory settings, particularly virology labs. As biohazard complexity increases, minimizing direct human exposure while maintaining precision becomes essential. We propose GAMORA (Gesture Articulated Meta Operative Robotic Arm), a novel VR-guided robotic system that enables remote execution of hazardous tasks using natural hand gestures. Unlike existing scripted automation or traditional teleoperation, GAMORA integrates the Oculus Quest 2, NVIDIA Jetson Nano, and Robot Operating System (ROS) to provide real-time immersive control, digital twin simulation, and inverse kinematics-based articulation. The system supports VR-based training and simulation while executing precision tasks in physical environments via a 3D-printed robotic arm. Inverse kinematics ensure accurate manipulation for delicate operations such as specimen handling and pipetting. The pipeline includes Unity-based 3D environment construction, real-time motion planning, and hardware-in-the-loop testing. GAMORA achieved a mean positional discrepancy of 2.2 mm (improved from 4 mm), pipetting accuracy within 0.2 mL, and repeatability of 1.2 mm across 50 trials. Integrated object detection via YOLOv8 enhances spatial awareness, while energy-efficient operation (50% reduced power output) ensures sustainable deployment. The system's digital-physical feedback loop enables safe, precise, and repeatable automation of high-risk lab tasks. GAMORA offers a scalable, immersive solution for robotic control and biosafety in biomedical research environments.

Figures

Figures reproduced from arXiv: 2506.14513 by the authors.

Figure 1
Figure 1. System Workflow A. Hardware SetUp The proposed hardware setup, shown in Fig.2, is designed to safely link the hazardous lab environment with the operator’s remote interface. Within the hazardous zone, a 5-DOF robotic arm performs critical handling of infectious materials, guided by commands processed on a Jetson Nano and actuated via an Arduino DUE. A Ricoh Theta SC2 camera provides live video feedback to the operat… view at source ↗
Figure 2
Figure 2. Hardware Setup IV. METHODOLOGY A. Creation of the Virtual Environment The virtual environment for GAMORA was developed following a structured pipeline, as illustrated in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Virtual Workspace B. Configuration of the Robotic Arm The design of the GAMORA robotic arm followed a com￾prehensive digital-to-physical workflow, as shown in Fig.5. The arm was first modeled using SolidWorks to define its kine￾matic structure, joint constraints, and end-effector geometry , as shown in Fig.5a. The components were then fabricated us￾ing 3D printing, shown in Fig. 5b, ensuring accurate translation of … view at source ↗
Figures from the paper (5 more)
Figure 3
Figure 3. Figure 3: Process flow for creating virtual workspace [PITH_FULL_IMAGE:figures/full_fig_p003_3.png]
Figure 6
Figure 6. Figure 6: Hardware and software architecture with ROS packages [PITH_FULL_IMAGE:figures/full_fig_p004_6.png]
Figure 7
Figure 7. Figure 7: Path planning in robotic arm using Inverse Kinematics [PITH_FULL_IMAGE:figures/full_fig_p004_7.png]
Figure 8
Figure 8. Figure 8: Comparative performance metrics of the original and [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]
Figure 9
Figure 9. Figure 9: Repeatability analysis of the GAMORA system over [PITH_FULL_IMAGE:figures/full_fig_p006_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

41 extracted references · 34 canonical work pages

  1. [1]

    The peculiar characteristics and advancement in diagnostic methodologies of influenza a virus,

    M. A. Raza, M. A. Ashraf, M. N. Amjad, G. U. Din, B. Shen, and Y . Hu, “The peculiar characteristics and advancement in diagnostic methodologies of influenza a virus,” Frontiers in Microbiology, vol. 15, Jan. 2025. [Online]. Available: http://dx.doi.org/10.3389/fmicb.2024.1435384

    work page arXiv 2025

  2. [2]

    Medical viruses: diagnostic techniques,

    Harsh and P. Tripathi, “Medical viruses: diagnostic techniques,” Virology Journal, vol. 20, no. 1, Jul. 2023. [Online]. Available: http://dx.doi.org/10.1186/s12985-023-02108-w

    work page doi:10.1186/s12985-023-02108-w 2023

  3. [3]

    Dengue virus biosafety: An analysis of evidence, global inconsistencies, and risk gaps,

    K. Kim Le and S. D. Blacksell, “Dengue virus biosafety: An analysis of evidence, global inconsistencies, and risk gaps,”Applied Biosafety, Mar. 2025. [Online]. Available: http://dx.doi.org/10.1089/apb.2024.0065

    work page arXiv 2025

  4. [4]

    Tracking the threat, 50 years of laboratory- acquired infections: A systematic review,

    E. Zavaleta-Monestel, C. Rojas-Chinchilla, A. Anch ´ıa-Alfaro, D. Quesada-Lor ´ıa, J. Garc ´ıa-Montero, S. Arguedas-Chac ´on, and G. Hanley-Vargas, “Tracking the threat, 50 years of laboratory- acquired infections: A systematic review,”Acta Microbiologica Hellenica, vol. 70, no. 2, p. 11, Mar. 2025. [Online]. Available: http://dx.doi.org/10.3390/amh70020011

    work page doi:10.3390/amh70020011 2025

  5. [5]

    Laboratory-associated infections and biosafety,

    P. Berche, “Laboratory-associated infections and biosafety,”La Presse M ´edicale, p. 104277, Apr. 2025. [Online]. Available: http://dx.doi.org/10.1016/j.lpm.2025.104277

    arXiv 2025

  6. [6]

    Bolstering biosafety: Analyzing laboratory-acquired infections and biosafety protocols,

    S. Ghose, “Bolstering biosafety: Analyzing laboratory-acquired infections and biosafety protocols,” 2024. [Online]. Available: http://dx.doi.org/10.2139/ssrn.4864642

    work page doi:10.2139/ssrn.4864642 2024

  7. [8]

    Rebia—robotic enabled biological automation: 3d epithelial tissue production,

    L. K ¨oniger, C. Malkmus, D. Mahdy, T. D ¨aullary, S. G ¨otz, T. Schwarz, M. Gensler, N. Pallmann, D. Cheufou, A. Rosenwald, M. M ¨ollmann, D. Groneberg, C. Popp, F. Groeber-Becker, M. Steinke, and J. Hansmann, “Rebia—robotic enabled biological automation: 3d epithelial tissue production,”Advanced Science, vol. 11, no. 45, Sep

    work page

  8. [9]

    Robots in the laboratory: a review of applications,

    R. Bogue, “Robots in the laboratory: a review of applications,”Industrial Robot: An International Journal, vol. 39, no. 2, p. 113–119, Mar. 2012. [Online]. Available: http://dx.doi.org/10.1108/01439911211203382

    work page doi:10.1108/01439911211203382 2012

Show all 41 references
  1. [10]

    A novel affordable user interface for robotic surgery training: design, development and usability study,

    A. Neri, M. Coduri, V . Penza, A. Santangelo, A. Oliveri, E. Turco, M. Pizzirani, E. Trinceri, D. Soriero, F. Boero, S. Ricci, and L. S. Mattos, “A novel affordable user interface for robotic surgery training: design, development and usability study,”Frontiers in Digital Healt...

  2. [11]

    Medical training in virtual reality: a gamification approach,

    L. Turchet, F. Gentilini, S. Malandra, A. Veccia, A. Antonelli, and C. Scoffone, “Medical training in virtual reality: a gamification approach,”Virtual Reality, vol. 29, no. 2, May 2025. [Online]. Available: http://dx.doi.org/10.1007/s10055-025-01159-4

    2025 doi

  3. [12]

    Measuring the effectiveness of virtual training: A systematic review,

    P. Strojny and N. Du ˙zma´nska-Misiarczyk, “Measuring the effectiveness of virtual training: A systematic review,”Computers & amp; Education: X Reality, vol. 2, p. 100006, 2023. [Online]. Available: http://dx.doi.org/10.1016/j.cexr.2022.100006

    2023

  4. [13]

    Available: http://dx.doi.org/10.3389/fdgth.2024.1428534

    [Online]. Available: http://dx.doi.org/10.3389/fdgth.2024.1428534

    2024

  5. [14]

    Modern virtual reality headsets,

    V . Angelov, E. Petkov, G. Shipkovenski, and T. Kalushkov, “Modern virtual reality headsets,” in2020 International Congress on Human- Computer Interaction, Optimization and Robotic Applications (HORA), 2020, pp. 1–5

    2020

  6. [15]

    Enhancing hand-object interactions in virtual reality for precision manual tasks,

    M. Mangalam, S. Oruganti, G. Buckingham, and C. W. Borst, “Enhancing hand-object interactions in virtual reality for precision manual tasks,”Virtual Reality, vol. 28, no. 4, Nov. 2024. [Online]. Available: http://dx.doi.org/10.1007/s10055-024-01055-3

    2024 doi

  7. [16]

    Immersive virtual reality health games:a narrative review of game design,

    G. Tao, B. Garrett, T. Taverner, E. Cordingley, and C. Sun, “Immersive virtual reality health games:a narrative review of game design,”Journal of NeuroEngineering and Rehabilitation, vol. 18, no. 1, Feb. 2021. [Online]. Available: http://dx.doi.org/10.1186/s12984-020-00801-3

    2021 doi

  8. [17]

    Testing robot teleoperation using a virtual reality interface with ros reality,

    E. Rosen, D. Whitney, E. Phillips, D. Ullman, and S. Tellex, “Testing robot teleoperation using a virtual reality interface with ros reality,” 03 2018

    2018

  9. [18]

    Augmentation of haptic feedback for teleoperated robotic surgery,

    P. Schleer, P. Kaiser, S. Drobinsky, and K. Radermacher, “Augmentation of haptic feedback for teleoperated robotic surgery,” International Journal of Computer Assisted Radiology and Surgery, vol. 15, no. 3, p. 515–529, Jan. 2020. [Online]. Available: http://dx.doi.org/10.1007/...

    2020 doi

  10. [19]

    Design and development of a low-cost 5-dof robotic arm for lightweight material handling and sorting applications: A case study for small manufacturing industries of pakistan,

    Z. Ali, M. F. Sheikh, A. Al Rashid, Z. U. Arif, M. Y . Khalid, R. Umer, and M. Koc ¸, “Design and development of a low-cost 5-dof robotic arm for lightweight material handling and sorting applications: A case study for small manufacturing industries of pakistan,”Results in Eng...

    2023

  11. [20]

    Progress in robotics for combating infectious diseases,

    A. Gao, R. R. Murphy, W. Chen, G. Dagnino, P. Fischer, M. G. Gutierrez, D. Kundrat, B. J. Nelson, N. Shamsudhin, H. Su, J. Xia, A. Zemmar, D. Zhang, C. Wang, and G.-Z. Yang, “Progress in robotics for combating infectious diseases,”Science Robotics, vol. 6, no. 52, Mar

  12. [21]

    A systematic review with narrative synthesis on medical robotics and laboratory automation in the control of sars-cov-2, ebola and h1n1 (swine flu) viruses,

    S. Kannan, K. Subbaram, S. Ali, and H. Kannan, “A systematic review with narrative synthesis on medical robotics and laboratory automation in the control of sars-cov-2, ebola and h1n1 (swine flu) viruses,”Journal of Health and Social Sciences, vol. 5, 05 2020

    2020

  13. [22]

    Analysis of mr–vr tele-operation methods for legged-manipulator robots,

    C. Cruz Ulloa, D. Dom ´ınguez, J. del Cerro, and A. Barrientos, “Analysis of mr–vr tele-operation methods for legged-manipulator robots,”Virtual Reality, vol. 28, no. 3, Jul. 2024. [Online]. Available: http://dx.doi.org/10.1007/s10055-024-01021-z

    2024 doi

  14. [23]

    Roboculture: A robotics platform for automated biological experimentation,

    K. Angers, K. Darvish, N. Yoshikawa, S. Okhovatian, D. Bannerman, I. Yakavets, F. Shkurti, A. Aspuru-Guzik, and M. Radisic, “Roboculture: A robotics platform for automated biological experimentation,” 2025. [Online]. Available: https://arxiv.org/abs/2505.14941

    2025 arXiv

  15. [24]

    Available: http://dx.doi.org/10.1126/scirobotics.abf1462

    [Online]. Available: http://dx.doi.org/10.1126/scirobotics.abf1462

    doi

  16. [25]

    Research on key technologies base unity3d game engine,

    J. Xie, “Research on key technologies base unity3d game engine,” in 2012 7th International Conference on Computer Science & Education (ICCSE), 2012, pp. 695–699

    2012

  17. [26]

    Automation in the life science research laboratory,

    I. Holland and J. A. Davies, “Automation in the life science research laboratory,”Frontiers in Bioengineering and Biotechnology, vol. 8, Nov

  18. [27]

    Unity and ros as a digital and communication layer for digital twin application: Case study of robotic arm in a smart manufacturing cell,

    M. Singh, J. Kapukotuwa, E. L. S. Gouveia, E. Fuenmayor, Y . Qiao, N. Murry, and D. Devine, “Unity and ros as a digital and communication layer for digital twin application: Case study of robotic arm in a smart manufacturing cell,”Sensors, vol. 24, no. 17, p. 5680, Aug. 2024. ...

    2024 doi

  19. [28]

    Digital twin of robot manipulator using ros,

    S. K. Chinnasamy, H. P. Sura, A. Saleem, A. Kathirvel, and P. Rangan, “Digital twin of robot manipulator using ros,” inIV INTERNATIONAL SCIENTIFIC FORUM ON COMPUTER AND ENERGY SCIENCES (WFCES II 2022), vol. 2948. AIP Publishing, 2023, p. 040004. [Online]. Available: http://dx....

    2022 doi

  20. [29]

    Ros2unity3d; high-performance plugin to interface ros with unity3d engine,

    E. Babaians, M. Tamiz, Y . Sarfi, A. Mogoei, and E. Mehrabi, “Ros2unity3d; high-performance plugin to interface ros with unity3d engine,” in2018 9th Conference on Artificial Intelligence and Robotics and 2nd Asia-Pacific International Symposium, 2018, pp. 59–64

    2018

  21. [30]

    X. Wang, “Enabling human-robot partnerships in digitally-driven construction work through integration of building information models, interactive virtual reality, and process-level digital twins,” 2022. [On- line]. Available: http://deepblue.lib.umich.edu/handle/2027.42/174292

    2022

  22. [31]

    Comparative analysis of ros-unity3d and ros-gazebo for mobile ground robot simulation,

    J. Platt and K. Ricks, “Comparative analysis of ros-unity3d and ros-gazebo for mobile ground robot simulation,”Journal of Intelligent & amp; Robotic Systems, vol. 106, no. 4, Dec. 2022. [Online]. Available: http://dx.doi.org/10.1007/s10846-022-01766-2

    2022 doi

  23. [32]

    A systematic review of xr- enabled remote human-robot interaction systems,

    X. Wang, L. Shen, and L.-H. Lee, “A systematic review of xr- enabled remote human-robot interaction systems,”ACM Computing Surveys, vol. 57, no. 11, p. 1–37, Jun. 2025. [Online]. Available: http://dx.doi.org/10.1145/3730574

    2025 doi

  24. [33]

    Communicating and controlling robot arm motion intent through mixed-reality head-mounted displays,

    E. Rosen, D. Whitney, E. Phillips, G. Chien, J. Tompkin, G. Konidaris, and S. Tellex, “Communicating and controlling robot arm motion intent through mixed-reality head-mounted displays,”The International Journal of Robotics Research, vol. 38, no. 12–13, p. 1513–1526, Apr

  25. [34]

    A novel ros2 qos policy-enabled synchronizing middleware for co-simulation of heterogeneous multi-robot systems,

    E. Dey, M. Walczak, M. S. Anwar, N. Roy, J. Freeman, T. Gregory, N. Suri, and C. Busart, “A novel ros2 qos policy-enabled synchronizing middleware for co-simulation of heterogeneous multi-robot systems,” in2023 32nd International Conference on Computer Communications and Netwo...

    2023

  26. [35]

    Vr controlled remote robotic teleoperation for construction applications,

    C. Chan, A. Pelosi, and A. Brown, “Vr controlled remote robotic teleoperation for construction applications,” 2023

    2023

  27. [36]

    Virtual reality in human- robot interaction: Challenges and benefits,

    Y . Lei, Z. Su, and C. Cheng, “Virtual reality in human- robot interaction: Challenges and benefits,”Electronic Research Archive, vol. 31, no. 5, p. 2374–2408, 2023. [Online]. Available: http://dx.doi.org/10.3934/era.2023121

    2023 doi

  28. [37]

    A teach pendant to control virtual robots in roboanalyzer,

    I. Mehta, K. Bimbraw, R. G. Chittawadigi, and S. K. Saha, “A teach pendant to control virtual robots in roboanalyzer,” in2016 International Conference on Robotics and Automation for Humanitarian Applications (RAHA), 2016, pp. 1–6

    2016

  29. [40]

    Novel software for high-level virological testing: Self-designed immersive virtual reality training approach,

    H.-P. Tsai, C.-W. Lin, Y .-J. Lin, C.-S. Yeh, and Y .-S. Shan, “Novel software for high-level virological testing: Self-designed immersive virtual reality training approach,”Journal of Medical Internet Research, vol. 25, p. e44538, Jun. 2023. [Online]. Available: http://dx.doi...

    2023 doi

  30. [42]

    Evaluating the use of virtual reality on professional robotics education,

    M. C. Char ˜ao dos Santos, V . A. Sangalli, and M. S. Pinho, “Evaluating the use of virtual reality on professional robotics education,” in2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), vol. 1, 2017, pp. 448–455

    2017

  31. [2019]

    Available: http://dx.doi.org/10.1177/0278364919842925

    [Online]. Available: http://dx.doi.org/10.1177/0278364919842925

    doi

  32. [2020]

    Available: http://dx.doi.org/10.3389/fbioe.2020.571777

    [Online]. Available: http://dx.doi.org/10.3389/fbioe.2020.571777

    2020

  33. [2024]

    Available: http://dx.doi.org/10.1002/advs.202406608

    [Online]. Available: http://dx.doi.org/10.1002/advs.202406608

    doi

Pith tools

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