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REVIEW 4 major objections 5 minor 46 references

From Pixels to Shelf: An Integrated Robotic System for Autonomous Supermarket Stocking with a Mobile Manipulator

T0 review · 4 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read A robot built from commodity hardware can autonomously stock supermarket shelves with 98% object-level reliability, though it remains ten times slower than human workers.

desk verdict A genuinely useful, honest systems paper on retail restocking; the 98.3% headline is plausible but not yet verifiable because the paper never pins down what counts as a success. read the letter →

arxiv 2509.11740 v2 pith:JYBFV6EQ submitted 2025-09-15 cs.RO

Davide Peron , Victor Nan Fernandez-Ayala , Lukas Segelmark This is my paper · ORCID
classification cs.RO
keywords autonomousshelfstockingmobilemanipulationbehaviortreesmodelpredictivecontrolobjectdetectionvisuallocalizationperformancebenchmarkingretailautomation
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 sets out to show that autonomous supermarket shelf stocking can be done by a mobile manipulator built from affordable, off-the-shelf parts, without requiring a bespoke research platform. It reports 98.3% object-level success across 724 pick-and-place operations in a mock store run as an overnight closed-door scenario, with a mean full-pipeline time of 68.2 seconds per item. The same experiments benchmark the robot against teleoperation and human workers, showing that autonomy beats teleoperation on a combined time-cost index but still trails human stocking by roughly a factor of ten. The value of the paper is less the absolute numbers than the reproducible baseline: it identifies where the gap lives and openly measures it.

What carries the argument

The mechanism that carries the argument is the 'last-meter' navigation stack: a 14-state Kalman filter fusing ArUco marker poses with a constant-velocity model feeds a two-step Model Predictive Controller that drives the robot base toward the shelf while steering the pan-tilt head to keep the marker in view. This solves the problem that the base lidar cannot see objects closer than 20 cm. Around that core, a Behavior Tree orchestrates docking, navigation, picking, and planogram-aware placement, while perception uses SAM2 segmentation, fine-tuned YOLO per-SKU classifiers, and an optional GPT-4o fallback for products without a trained model.

What would settle it

Run the same pipeline in a store aisle where items are spaced ~3 mm apart and include a wider range of package geometries; if object-level success drops materially below 98% for a comparable number of events, the central claim fails. A cheaper, paper-consistent check: count failures among products without a dedicated YOLO model—the paper itself predicts this is where all failures occur.

Watch

Extended reading notes

Core claim

The central claim is that a modular pipeline—behavior-tree task planning, ArUco-marker localization fused through a Kalman filter, a two-step model-predictive controller for base and head camera, and a perception stack pairing fine-tuned YOLO classifiers with SAM2 segmentation and tracking—can carry a Stretch 3 mobile manipulator through the full pick-navigate-place cycle in a supermarket-like environment with 98.3% object-level success over 724 events and a mean of 68.2 s per item. The paper further argues that most remaining failures come from products without a dedicated YOLO model, that task-level success reaches 87%, and that a joint cost-time index places autonomous stocking between te

Load-bearing premise

The 98% success rate depends on simplified products (cans and small boxes) and simplified packing (3 cm spacing instead of the ~3 mm common in stores); if those constraints are removed, the figure is not guaranteed by the paper's data.

Editorial extensions

If this is right

  • If the 98.3% reliability transfers beyond the mock store, overnight restocking of canned and boxed goods is economically plausible: the robot's cost-time index is about a third of human performance but an order of magnitude better than teleoperation.
  • The failure concentration among products lacking a dedicated YOLO model implies that expanding the per-SKU model library is the most direct route to higher task-level success.
  • The 78%/22% hardware/software decomposition suggests that gripper and depth-sensing upgrades will shrink the speed gap more than any algorithmic change.
  • The open-sourced modules and simulator give other groups a concrete baseline to test new perception or control components against the same 68.2 s/item and 98.3% figures.

Reading between the lines

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

  • The reported experiments use 3 cm spacing between placed items, whereas real store shelves commonly pack items at about 3 mm; until the same pipeline is tested at real packing density, 98% should be read as an upper bound.
  • The GPT-4o fallback's low accuracy and 6.9 s inference time, versus 0.2 s for fine-tuned YOLO, suggests an inexpensive testable improvement: an online mechanism that flags repeatedly-misclassified SKUs for model training, rather than relying on a generic vision-language model.
  • If two or more of these robots were coordinated overnight in a single store, the architecture's task queue could scale throughput almost linearly; the paper does not address multi-robot collision or task allocation, but nothing in the reported design prevents that experiment.
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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 / 5 minor

Summary. The paper presents an integrated autonomous supermarket stocking system built on the Hello Robot Stretch 3 mobile manipulator. The architecture combines ROS2, Behavior Trees, Nav2 with a spatio-temporal voxel layer, ArUco-based localization with a Kalman filter, a two-step MPC for base/head control, and a perception pipeline using YOLO and SAM2. The authors report laboratory experiments in a mock supermarket: 70 stocking tasks totaling 724 individual stocking operations, an object-level success rate exceeding 98% (and 98.3% in the discussion), a task-level success rate above 87%, and comparisons against teleoperation and human workers, including a joint time-cost performance index and a hardware/software gap decomposition. Several components are released as open-source ROS2 packages.

Significance. If the reported success rates are reproducible and well-defined, the paper would provide a useful baseline for an affordable, integrated retail stocking platform and a quantitative comparison against humans and teleoperation. The open-source release of the docking, navigation, and simulator packages is a concrete strength, as is the focus on a commercially available robot rather than custom high-end hardware. The paper is primarily a systems integration contribution, and the experimental evaluation is central to its claims.

major comments (4)
  1. [Sec. V.C–V.D] The central claim '98.3% object-level success, including pick and place operations and shelf navigation' is not verifiable as stated. No success rubric is defined: what counts as a successful stocking operation at the object level (placement tolerance, orientation, contact, gripper release, planogram compliance)? No raw success/failure counts by stage are given, and no confidence intervals are reported. The text says 8 of 9 unsuccessful tasks were partial failures due to 'minor placement inaccuracies' but does not say whether those inaccuracies are scored as object-level successes or failures. One task completely failed due to total loss of the ArUco marker; the number of objects in that task and whether they are in the 724 denominator are not stated. The phrase 'including pick and place operations and shelf navigation' is also unsupported: the experimental section reports individual sto
  2. [Sec. V.B] The reported 98% success rate is conditioned on a narrow product set and an artificial spacing requirement: 'Due to gripper size ... we focused on cans and small boxes' and 'placement required a spacing of 3cm between items, exceeding the common practice of approximately 3mm.' The paper also states that 'all failures involved products lacking a dedicated YOLO model,' meaning failures are concentrated in the GPT-4o-based perception branch. The claim that the system demonstrates 'reliable performance' for supermarket stocking is therefore broader than the evidence supports. The claims should be restricted to the tested product/spacing regime, or additional experiments under tighter spacing and with a more diverse product set should be provided.
  3. [Sec. V.D] The hardware/software gap decomposition—'approximately 78% of the lost performance should be attributed to hardware constraints and 22% to software limitations'—is asserted without a method. No equation, data table, sensitivity analysis, or error propagation is provided to show how these percentages are derived from the timing data. This decomposition is presented as one of the paper's contributions ('a hardware/software gap decomposition outlining concrete paths toward commercially viable retail robots'). It needs a transparent calculation or should be removed.
  4. [Sec. V.C] The statistical evaluation is incomplete. The paper reports point estimates (98% object-level, 87% task-level) over 70 tasks and 724 operations but gives no per-task variation, no confidence intervals, and no error bars in Fig. 7. The comparisons with Spahn et al. [6] and Wu et al. [11] cite their success rates without adjusting for different protocols or task definitions. Please add confidence intervals for the proportions (e.g., Clopper-Pearson) and clearly state the number of objects per task and the definition of task success, so the headline numbers are interpretable and comparable.
minor comments (5)
  1. [Introduction] The text says 'Finally, Section IV presents extensive laboratory tests' but the experiments are presented in Section V. The section numbering is inconsistent.
  2. [Sec. V.D] The heading 'Performace' should be 'Performance'.
  3. [Algorithm 1] Typos in function names: 'Get_Bouning_Boxes' appears twice; should be 'Get_Bounding_Boxes'.
  4. [Eq. (13)] The performance index 'pi = 1000 / (Annual Cost · Time Per Item)' would benefit from explicit units and a definition of Annual Cost (USD/year) and Time Per Item (seconds). The text uses p_i,AR, p_i,H, p_i,T without defining the subscript convention.
  5. [Sec. III.A] Minor typo: 'unicyle' should be 'unicycle'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central results are empirical measurements, and the cited self-work is not load-bearing.

full rationale

The paper's headline quantitative claims—98.3% object-level success over 724 stocking operations and 68.2 s per item—are empirical measurements from the integrated system, not derived quantities in which an output is recycled from an input. The control and estimation components (KF, two-step MPC, visual servo gains) use standard textbook formulations (Secs. II-B, II-C, IV-C), with parameters tuned from logs or trial-and-error; the reported success rates are subsequent independent experiments, so no fitted parameter is renamed as a prediction. The only self-citations are [2], an introductory reference for multi-robot agriculture that is not load-bearing, and [35], cited for an ArUco field-of-view safety constraint in the head MPC (Sec. IV-C.2, near Eq. (12)). Even if [35] is by a co-author, it is used as a constraint reference, not as a uniqueness theorem or as the source of the success claim. The paper's stated limitations—3 cm spacing, canned/small-box focus, and failures concentrated on products without dedicated YOLO models—affect external validity and metric interpretability, but they do not make the experimental results circular. I find no step where a claimed result is equivalent by construction to an input or where a prediction is statistically forced by a fitted parameter.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central claims rest on disclosed domain simplifications and tuned parameters rather than on new theoretical entities. The most consequential simplifications are product geometry/spacing and the overnight no-bystander regime.

free parameters (5)
  • Docking visual servoing gains K_x, K_y = 0.75, -5.0
    Chosen for well-damped response (Sec. V.B); affect docking success.
  • Manipulation velocity gains P_b, P_l, P_a = 0.0005, 0.003, 0.9
    Tuned by trial and error on the robot (Sec. IV-E.1).
  • KF noise covariances sigma_x, sigma_z = 0.5
    Estimated from short static ArUco pose jitter log (Sec. V.B).
  • MPC weights Q, R, K and horizon N
    Cost functions (11) and (12) require these; values not reported.
  • YOLO_grocery training hyperparameters = 200 epochs, lr 0.005 to 0.0001
    Chosen training setup; model weights not released.
assumptions (6)
  • domain assumption The mock supermarket with pre-mapped layout, known cart position, and no human bystanders is representative of the intended overnight deployment scenario.
    Sec. V: 'we target overnight stocking with doors closed... environment is consistent... no bystanders.' This limits generalization to daytime operation.
  • domain assumption Products are limited to cans and small boxes that fit the 2.5 cm gripper and can be spaced at 3 cm (or about 5 mm for cylinders).
    Sec. V.B: selection ensures graspability and placement tolerance; excludes fragile, deformable, or tightly packed goods.
  • domain assumption Known GTIN product dimensions and planogram locations are available and correct.
    Used by Get_Prod_Info and Query_Planogram; placement planning depends on this.
  • domain assumption ArUco markers installed in shelf label rails have known world-frame poses and remain visible; if lost, task fails.
    Sec. III-B and Sec. V.C: the only complete task failure was due to total loss of the ArUco marker.
  • standard math Standard linear Kalman filter and MPC formulations with Gaussian noise and constant-velocity models are appropriate for the pose-estimation and navigation problem.
    Secs. II-B, II-C, IV-C.1; standard but unproved in this context.
  • domain assumption Cost model assumptions: USD 25,000 robot, 5,000-hour lifespan, 8-hour shifts, 2 hours weekly maintenance, and literature-based human labor costs.
    Sec. V.D; if these change, the estimated cost advantage over human workers changes.

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Cite this review

Pith. "Pith review of From Pixels to Shelf: An Integrated Robotic System for Autonomous Supermarket Stocking with a Mobile Manipulator." pith.science (2026). https://pith.science/paper/JYBFV6EQ

@misc{pith2026250911740,
  author       = {Pith},
  title        = {Pith review of: From Pixels to Shelf: An Integrated Robotic System for Autonomous Supermarket Stocking with a Mobile Manipulator},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JYBFV6EQ}},
  note         = {Machine review of arXiv:2509.11740}
}
read the original abstract

Autonomous stocking in retail environments, particularly supermarkets, presents challenges due to dynamic human interactions, constrained spaces, and diverse product geometries. This paper introduces an efficient modular robotic system for autonomous shelf stocking, integrating commercially available hardware with a scalable algorithmic architecture. A major contribution of this work is the system integration of off-the-shelf hardware and ROS2-based perception, planning, and control into a single deployable platform for retail environments. Our solution leverages Behavior Trees (BTs) for task planning, fine-tuned vision models for object detection, and a two-step Model Predictive Control (MPC) framework for precise shelf navigation using ArUco markers. Laboratory experiments replicating realistic supermarket conditions demonstrate reliable performance, achieving over 98% success in pick-and-place operations across a total of more than 700 stocking events. However, our comparative benchmarks indicate that the performance and cost-effectiveness of current autonomous systems remain inferior to that of human workers, which we use to highlight key improvement areas and quantify the progress still required before widespread commercial deployment can realistically be achieved.

Figures

Figures reproduced from arXiv: 2509.11740 by the authors.

Figure 1
Figure 1. Behaviour Trees (BTs). shelf-proximal navigation and manipulation reliability via visual markers plus MPC and improved perception, achieving 98% success over 724 pick-place events with autonomous docking/charging on an affordable robotic platform. Third, we provide a detailed validation in an overnight closed-door operation scenario under standard indoor lighting, matching retailer practice to avoid customer congest… view at source ↗
Figure 2
Figure 2. Hardware overview. B. Supermarket Setting We performed experiments within a mock supermarket environment (Fig. 2b). The products to be stocked are assumed to be placed on a mobile cart previously positioned near the target shelves by our robot. To guarantee a reliable pose estimation without affecting realistic store operations, we placed small ArUco tags in the store shelf label holders. These visual markers, read … view at source ↗
Figure 3
Figure 3. Planning and communication diagram. B. Navigation Our open-source navigation module [24] uses the open￾source ROS2 Nav2 package [22], which provides a modular framework for autonomous robot navigation that integrates various planners and controllers. We use a Model Predic￾tive Path Integral (MPPI) controller [32], which ensures smooth, real-time trajectories. Additionally, we introduced a STVL [31] to the navigation… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Nav2 interface (left) and the simulator (right). [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Overview of the pick and place task. (a) Segment RGB image. (b) Detect correct products. (c) Bounding box select. (d) Track selected product. (e) Pick using depth image [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Overview of the perception pipeline used for picking. [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 8
Figure 8. Figure 8: Comparison of cost and performance. Integrating advanced models like LLM showed impractical real-time performance, although using task-specific YOLO￾based models improved picking times by about 6s per item (Tab. I), emphasizing the value of tailored solutions. Both rob…
Figure 7
Figure 7. Figure 7: Reliability analysis on 70 tasks and 724 objects. D. Performace We evaluated the autonomous stocking system’s perfor￾mance against teleoperation and manual human stocking in terms of stocking time and annual operational cost ( [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]

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Reference graph

Works this paper leans on

46 extracted references · 4 linked inside Pith

  1. [6]

    Demonstrating adap- tive mobile manipulation in retail environments,

    M. Spahn, C. Pezzato, C. Salmi, R. Dekker, C. Wang, C. Pek, J. Kober, J. Alonso-Mora, C. H. Corbato, and M. Wisse, “Demonstrating adap- tive mobile manipulation in retail environments,”Proceedings of the Robotics: Science and System XX, 2024

    2024

  2. [11]

    In the wild ungraspable object picking with bimanual nonprehensile manipulation,

    A. Wu and D. Kruse, “In the wild ungraspable object picking with bimanual nonprehensile manipulation,” 2024. [Online]. Available: https://arxiv.org/abs/2409.15465

    arXiv 2024

  3. [1]

    Robotics in manufacturing: A review of advances in automation and workforce implications,

    R. Adebayo, C. Nwankwo, I. Festus-Ikhuoria, and O. Olajiga, “Robotics in manufacturing: A review of advances in automation and workforce implications,”International Journal of Advanced Multidis- ciplinary Research and Studies, vol. 4, pp. 632–638, 03 2024

    2024

  4. [2]

    Efficient coordination and synchronization of multi- robot systems under recurring linear temporal logic,

    D. Peron, V . N. Fernandez-Ayala, E. E. Vlahakis, and D. V . Di- marogonas, “Efficient coordination and synchronization of multi- robot systems under recurring linear temporal logic,” in2025 IEEE International Conference on Robotics and Automation (ICRA), 2025, pp. 10 194–10 200

    2025

  5. [3]

    53-7065 stockers and order fillers,

    U.S. Bureau of Labor Statistics, “53-7065 stockers and order fillers,” Mar. 2024, accessed: 2025-03-24. [Online]. Available: https://www.bls.gov/oes/2023/may/oes537065.htm

    2024

  6. [4]

    Forecasting workforce for store attendants in a grocery retailer,

    E. Z. Güney, “Forecasting workforce for store attendants in a grocery retailer,”KNOWLEDGE-International Journal, vol. 31, no. 1, pp. 293– 297, 2019

    2019

  7. [5]

    Dhaliwal,The Rise of Automation and Robotics in Warehouse Management

    A. Dhaliwal,The Rise of Automation and Robotics in Warehouse Management. CRC Press, 10 2020, pp. 63–72

    2020

  8. [7]

    Shelf replenishment based on object arrangement detection and collapse prediction for bimanual manipulation,

    T. Motoda, D. Petit, T. Nishi, K. Nagata, W. Wan, and K. Harada, “Shelf replenishment based on object arrangement detection and collapse prediction for bimanual manipulation,”Robotics, vol. 11, no. 5, 2022

    2022

Show all 46 references
  1. [8]

    An investigation towards an efficient algorithm for the automation of stocking and disposal tasks in a convenience store using a robotic arm system,

    F. Sasono and K. Wada, “An investigation towards an efficient algorithm for the automation of stocking and disposal tasks in a convenience store using a robotic arm system,”Journal of Robotics and Mechatronics, vol. 37, pp. 231–239, 02 2025

    2025

  2. [9]

    Bossa nova robotics,

    “Bossa nova robotics,” https://www.bossanova.com/bossa-nova

  3. [10]

    Practical insights on grasp strategies for mobile manipulation in the wild,

    I. Huang, R. Cheng, S. Kim, D. Kruse, C. Matl, L. Kaul, J. Hancock, S. Harikumar, M. Tjersland, J. Borders, and D. Helmick, “Practical insights on grasp strategies for mobile manipulation in the wild,”

  4. [12]

    Can robots refill a supermarket shelf?: Motion planning and grasp control,

    M. Costanzo, G. De Maria, G. Lettera, and C. Natale, “Can robots refill a supermarket shelf?: Motion planning and grasp control,”IEEE Robotics & Automation Magazine, vol. PP, pp. 2–14, 03 2021

    2021

  5. [13]

    Towards autonomous retail stocking and picking: Methods enabling robust vacuum-based robotic manipulation in densely packed environments,

    P. Kmecl, M. Munih, and J. Podobnik, “Towards autonomous retail stocking and picking: Methods enabling robust vacuum-based robotic manipulation in densely packed environments,”Sensors, vol. 24, no. 20, 2024

    2024

  6. [14]

    Shelf management: A deep learning-based system for shelf visual monitoring,

    R. Pietrini, M. Paolanti, A. Mancini, E. Frontoni, and P. Zingaretti, “Shelf management: A deep learning-based system for shelf visual monitoring,”Expert Systems with Applications, vol. 255, p. 124635,

  7. [15]

    An end-to-end computationally lightweight vision-based grasping system for grocery items,

    T. Mansakul, G. Tang, P. Webb, J. Rice, D. Oakley, and J. Fowler, “An end-to-end computationally lightweight vision-based grasping system for grocery items,”Sensors, vol. 25, no. 17, 2025. [Online]. Available: https://www.mdpi.com/1424-8220/25/17/5309

    2025

  8. [16]

    Hello robot stretch 3,

    “Hello robot stretch 3,” https://hello-robot.com/stretch-3-product

  9. [17]

    You only look once: Unified, real-time object detection,

    “You only look once: Unified, real-time object detection,” in2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788

    2016

  10. [18]

    Sam 2: Segment anything in images and videos,

    N. Ravi, V . Gabeur, Y .-T. Hu, R. Hu, C. Ryali, T. Ma, H. Khedr, R. Rädle, C. Rolland, L. Gustafson, E. Mintun, J. Pan, K. V . Alwala, N. Carion, C.-Y . Wu, R. Girshick, P. Dollár, and C. Feichtenhofer, “Sam 2: Segment anything in images and videos,”arXiv preprint arXiv:2408....

    2024 arXiv

  11. [19]

    Automatic generation and detection of highly reliable fiducial markers under occlusion,

    S. Garrido-Jurado, R. M. noz Salinas, F. Madrid-Cuevas, and M. Marín-Jiménez, “Automatic generation and detection of highly reliable fiducial markers under occlusion,”Pattern Recognition, vol. 47, no. 6, pp. 2280–2292, 2014. [Online]. Available: https: //www.sciencedirect.com/...

    2014

  12. [20]

    A new approach to linear filtering and prediction problems,

    R. E. Kalman, “A new approach to linear filtering and prediction problems,”Journal of Basic Engineering, vol. 82, no. 1, p. 35, 1960. [Online]. Available: http://dx.doi.org/10.1115/1.3662552

    1960 doi

  13. [21]

    Rawlings, D

    J. Rawlings, D. Mayne, and M. Diehl,Model Predictive Control: Theory, Computation, and Design. Nob Hill Publishing, 2017

    2017

  14. [22]

    The marathon 2: A navigation system,

    S. Macenski, F. Martin, R. White, and J. Ginés Clavero, “The marathon 2: A navigation system,” in2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020

    2020

  15. [23]

    https://github.com/animumai/ros2_stretch_docking

  16. [24]

    https://github.com/animumai/stretch_nav2_offloaded

  17. [25]

    https://github.com/animumai/ros2_iot_bridge

  18. [26]

    https://github.com/animumai/ros2_simulator_stretch

  19. [27]

    Behavior trees in robotics and ai,

    M. Colledanchise and P. Ögren, “Behavior trees in robotics and ai,” Jul

  20. [28]

    https://docs.ultralytics.com/models/yolo11

  21. [29]

    Ros2 - robot operating system 2,

    “Ros2 - robot operating system 2,” https://www.ros.org/

  22. [30]

    Banks, E

    A. Banks, E. Briggs, K. Borgendale, and R. Gupta,MQTT V ersion 5.0, OASIS Open Std., 03 2019, oASIS Standard. [Online]. Available: https://docs.oasis-open.org/mqtt/mqtt/v5.0/os/mqtt-v5.0-os.html

    2019

  23. [31]

    https://docs.nav2.org/tutorials/docs/navigation2_with_stvl.html

  24. [32]

    Aggressive driving with model predictive path integral control,

    G. Williams, P. Drews, B. Goldfain, J. M. Rehg, and E. A. Theodorou, “Aggressive driving with model predictive path integral control,” in 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 1433–1440

    2016

  25. [33]

    https://github.com/SteveMacenski/slam_toolbox

  26. [34]

    Visual servo control. i. basic approaches,

    F. Chaumette and S. Hutchinson, “Visual servo control. i. basic approaches,”Robotics & Automation Magazine, IEEE, vol. 13, pp. 82 – 90, 01 2007

    2007

  27. [35]

    Robust visual servoing under human supervision for assembly tasks,

    V . N. Fernandez-Ayala, J. Silva, M. Guo, and D. V . Dimarogonas, “Robust visual servoing under human supervision for assembly tasks,” 2025. [Online]. Available: https://arxiv.org/abs/2504.12506

    2025 arXiv

  28. [36]

    Solving automatic check-out with fine-tuned yolo models,

    R. Skinderowicz, “Solving automatic check-out with fine-tuned yolo models,”Procedia Computer Science, vol. 246, pp. 1649– 1658, 2024, 28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024). [Online]. Available: https://w...

    2024

  29. [37]

    GS1 General Specifications,

    GS1 AISBL, “GS1 General Specifications,” GS1 AISBL, Brussels, Belgium, Tech. Rep. Version 25.0, January 2025, the foundational GS1 standard defining identification keys (including GTIN/EAN), data attributes, application rules, and data carrier specifications for the global GS1...

    2025

  30. [38]

    Gpt-4 technical report,

    OpenAI, J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, R. Avila, I. Babuschkin, S. Balaji, V . Balcom, P. Baltescu, H. Bao, M. Bavarian, J. Belgum, and B. Zoph, “Gpt-4 technical report,” 03 2023

    2023

  31. [39]

    Inverse kinematics on stretch,

    H. Robot, “Inverse kinematics on stretch,” https://github.com/hello-r obot/stretch_tutorials/blob/master/stretch_body/jupyter/inverse_kine matics.ipynb, 03 2023

    2023

  32. [40]

    Fronta au- tonomous stocking,

    D. Peron, V . Nan Fernandez-Ayala, and L. Segelmark, “Fronta au- tonomous stocking,” Youtube. [Online]. Available: https://www.yout ube.com/watch?v=D6nUanEhqNc

  33. [41]

    Ros2 gazebo simulation

    C. U. C. Robotics, “Ros2 gazebo simulation.” [Online]. Available: https://github.com/CardiffUniversityComputationalRobotics/stretch_r os2/tree/humble/stretch_gz_sim

  34. [42]

    The supermarket, by department,

    Food Marketing Institute (FMI), “The supermarket, by department,” Web page, 2011, accessed: 2025-09-13. [Online]. Available: https: //www.fmi.org/docs/facts-figures/grocerydept.pdf

    2011

  35. [43]

    Table 4. employer costs for employee compensation for private industry workers by occupational and industry group,

    U. B. of Labor Statistics, “Table 4. employer costs for employee compensation for private industry workers by occupational and industry group,” 2024, accessed: 2025-04-07. [Online]. Available: https://www.bls.gov/news.release/ecec.t04.htm

    2024

  36. [2018]

    Available: http://dx.doi.org/10.1201/9780429489105

    [Online]. Available: http://dx.doi.org/10.1201/9780429489105

    doi

  37. [2024]

    Available: https://www.sciencedirect.com/science/arti cle/pii/S0957417424015021

    [Online]. Available: https://www.sciencedirect.com/science/arti cle/pii/S0957417424015021

  38. [2025]

    Available: https://arxiv.org/abs/2504.12512

    [Online]. Available: https://arxiv.org/abs/2504.12512

    arXiv

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Reviewed August 4, 2026 · model on record in the stance chip above.