Pith. sign in

REVIEW 2 major objections 5 minor 30 references

Autonomous Robotic Pepper Harvesting: Imitation Learning in Unstructured Agricultural Environments

T0 review · 2 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read Trained on 300 human demonstrations with a custom handheld shear-gripper, a diffusion-policy robot autonomously approaches, cuts, and grasps peppers in an unprotected field, reaching 28.95% success at 31.71s per cycle.

desk verdict First real outdoor field study of diffusion-policy pepper harvesting with a released dataset; the feasibility claim holds, but the headline comparability to greenhouse systems rests on mismatched task boundaries. read the letter →

arxiv 2411.09929 v1 pith:3EKD56GV submitted 2024-11-15 cs.RO

Chung Hee Kim , Abhisesh Silwal , George Kantor This is my paper · ORCID
classification cs.RO
keywords pepperharvestingimitationlearningdiffusionpolicyvisuomotoragriculturalroboticsoutdoorfielddeploymentfiducialcubetrackinggraspdetection
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 claims that imitation learning can carry robotic pepper harvesting out of the greenhouse and into an open, unstructured field. The authors collect 300 human demonstrations with a custom handheld shear-gripper, train a diffusion-policy visuomotor controller, and deploy it on a mobile manipulator in a working pepper field. The reported total success rate is 28.95% (64/221 trials) with a cycle time of 31.71 seconds, which they argue is comparable to previous greenhouse-based pepper-harvesting systems. The comparison rests on tagging easy peppers as equivalent to greenhouse studies' modified peppers and medium/hard peppers as equivalent to unmodified ones, and on defining success as approach/cut/grasp rather than pepper-in-crate.

What carries the argument

The core mechanism is the diffusion policy, a conditional denoising diffusion model that maps observation sequences (RGB images, gripper pose, and gripper actuation) to action sequences of gripper pose and actuation. To make outdoor demonstration collection feasible, the authors replace SLAM-based localization with a fiducial cube tracked by an external camera, using a six-step pose-refinement pipeline with SSIM filtering and corner refinement that reduces pose estimation error by about two orders of magnitude. A custom handheld shear-gripper, designed to mirror the robot's end-effector, collects the demonstrations, and a small CNN grasp detector decides when to hand control to an open-loop placement motion.

What would settle it

Re-run the same system while counting a trial as successful only when the pepper ends up in the crate, and have independent annotators label peppers using the greenhouse studies' criteria for modified versus unmodified; if the easy-only success rate no longer tracks the modified-pepper rates, or the medium/hard rate no longer tracks the unmodified rates, the 'comparable to greenhouse systems' claim fails.

Watch

Extended reading notes

Core claim

The central claim is that a diffusion-policy visuomotor model trained on 300 demonstrations collected with a custom handheld shear-gripper can autonomously approach, cut, and grasp peppers in an unprotected outdoor field, achieving a 28.95% success rate with a 31.71-second cycle time. The authors argue this is comparable to existing greenhouse systems, which operate under more controlled conditions. Success degrades with task difficulty: 42.57% for easy peppers, 20.21% for medium peppers, and 7.31% for hard peppers. The system also includes a separate CNN grasp detector that triggers an open-loop placement phase; the reported success metric covers approach, cut, and grasp but not the placement of the pepper into the crate.

Load-bearing premise

The comparability claim depends on the assumption that the paper's 'easy' peppers are equivalent to greenhouse studies' 'modified' peppers and that 'medium/hard' peppers are equivalent to 'unmodified' ones, an equivalence asserted without validation; success also excludes the open-loop placement phase.

Editorial extensions

If this is right

  • A single diffusion policy trained on 300 demonstrations generalizes across plots, lighting conditions, and plant variability in an outdoor field.
  • Policy success drops as difficulty rises: 42.57% easy, 20.21% medium, 7.31% hard, identifying peduncle shape and occlusion as the main bottlenecks.
  • The overall 28.95% success and 31.71-second cycle time are comparable to greenhouse pepper harvesters, despite the added difficulty of an unprotected field.
  • Replacing SLAM-based localization with refined fiducial-cube tracking makes demonstration collection feasible outdoors, reducing pose error by about two orders of magnitude.
  • The grasp detector's 0.83 accuracy, 0.71 precision, and 0.78 recall allow the policy to hand off to an open-loop placement controller.

Reading between the lines

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

  • A consequence the paper leaves implicit is that the released 300-demonstration dataset may become the more durable contribution, giving the field a shared outdoor benchmark for imitation-learning harvesters.
  • The paper's failure analysis suggests a concrete next test: condition the policy on an explicit peduncle detection signal; most of the 157 unsuccessful trials should shift into the success column if the bottleneck is fruit-targeting rather than actuation.
  • The reported emergence of re-grasping behavior not present in demonstrations implies diffusion policies can synthesize recovery strategies from demonstration data alone, a testable hypothesis for other deformable-object manipulation tasks such as berry picking.
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

2 major / 5 minor

Summary. The paper presents an imitation-learning-based robotic system for autonomous pepper harvesting in an unprotected outdoor field. The authors collect 300 human demonstrations with a custom handheld shear-gripper, train a diffusion policy visuomotor controller, and deploy it on a UR5e arm mounted on a Husky UGV. They evaluate the policy on 221 trials, reporting a 28.95% success rate (64/221) and an average cycle time of 31.71 s, and they compare these numbers with prior greenhouse systems in Table II. The paper also describes a robust fiducial-cube tracking method for data collection, a grasp detector for triggering open-loop placement, and a public dataset release.

Significance. If the reported results are taken at face value, the paper makes a useful contribution by demonstrating that imitation learning with a low-cost handheld data-collection device can produce a visuomotor policy that generalizes across outdoor field conditions, lighting variability, and crop diversity. The evaluation is transparent in reporting raw trial counts and held-out test rows, and the release of the 300-demonstration dataset is a concrete benefit to the community. The central feasibility claim—that a diffusion policy can perform the approach-cut-grasp-retract manipulation phase in an unstructured field—is supported by the data. However, the headline comparability to greenhouse systems is not yet established, because the task scope and difficulty definitions differ from the prior works in ways the paper does not validate.

major comments (2)
  1. [Sec. VI-B.1 and Sec. V-C] The success metric reported as 28.95% is defined only over the policy-controlled approach, cut, grasp, and retract phases, explicitly excluding the open-loop placement phase and starting after a manual setup in which a human positions the Husky and joysticks the end-effector until the target pepper is in view. The prior systems in Table II report harvesting success rates for complete harvest cycles that include detection, positioning, and placement. The abstract's statement that the system is 'comparable to existing systems tested under more controlled conditions like greenhouses' therefore rests on different task boundaries. The authors should either present a like-for-like comparison by including the placement and setup phases in the success definition, or explicitly qualify the comparison as applying only to the manipulation phase.
  2. [Sec. VI-C and Table II] The mapping of the authors' 'easy' difficulty category to the 'modified peppers' of prior works and of 'medium/hard' to 'unmodified peppers' is an unvalidated assumption. The prior works [18]–[20] modified peppers through pruning and repositioning, while the authors' difficulty categories are based on peduncle shape and occlusion. The paper provides no evidence that these two axes of difficulty correspond across studies. Because the entire comparability claim in Table II depends on this equivalence, the authors should either justify the mapping with quantitative similarity measures (e.g., comparable occlusion statistics or peduncle orientations) or remove the cross-study comparison and report only the internal difficulty breakdown.
minor comments (5)
  1. [Sec. VI-B.1] The hard-difficulty row reports only 2 successes out of 27 trials (7.31%); a binomial confidence interval would help the reader judge the stability of this estimate and would strengthen the internal comparison across difficulty levels.
  2. [Table II] The entry for [19] gives '25%' without a trial count; for consistency, provide the raw count or state explicitly that it is not available.
  3. [Sec. III-A] Equation (1) omits the variance schedule of the DDPM sampling process; clarifying the notation and the schedule would improve reproducibility.
  4. [Sec. IV-B] The SSIM threshold is a free parameter; state its value and, if possible, its sensitivity, since it directly affects the pose-tracking quality used for demonstration data.
  5. [Sec. II] There are minor grammatical errors, e.g., 'Advancements in robotics and deep learning is driving' should be 'are driving', and in Sec. III-A 'Diffusion policy offer' should be 'Diffusion policy offers'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the headline success rate is an out-of-sample empirical measurement, and self-citations are not load-bearing.

full rationale

The paper's central claim is an experimental result, not a derivation from a fitted parameter. The diffusion policy was trained on 300 demonstrations collected from 10 plots and then evaluated on 221 trials in 10 reserved plots (Sec. V-A: "We designated 2.5 rows (equivalent to 10 plots) for collecting demonstration data and reserved the remaining 2.5 rows (10 plots) for testing the robotic system"), so the reported 28.95% success rate is an out-of-sample measurement of a learned policy, not a quantity forced by the training data or by the definition of the policy objective. The success metric in Sec. VI-B.1 is explicitly scoped: "defining success as the completion of these tasks without considering the open-loop placement phase." This narrows what the number means and makes the comparison with prior greenhouse systems contestable on task-scope grounds, but it is not circular; the success event is not defined in terms of the policy's own outputs, and the placement phase is described as an additional open-loop controller rather than part of the claimed visuomotor policy result. The Table II comparison rests on an asserted mapping in Sec. VI-C: "we associate the easy category with modified peppers, while the combined medium and hard categories represent unmodified peppers." That is an unvalidated comparability assumption between the paper's difficulty labels and prior works' crop modifications, but it is not a fitted parameter renamed as a prediction and does not make the measured success rate equivalent to its inputs. Related-work self-citations [6], [8], [9], [10] provide context from the same group's prior agricultural robotics work and are not used to justify the central design or evaluation. The method sections rely on external prior work, UMI [3] and Diffusion Policy [4], with independent implementations and stated modifications; no load-bearing premise is established solely by a self-citation. No equation in the paper reduces a prediction to a training target by construction, and no fitted quantity is later reported as a prediction. The main caveats, such as manual setup and excluded placement, are explicitly disclosed in Sec. V-C and Sec. VIII and belong under correctness or scope risk, not circularity. The score is therefore 0: no significant circularity found.

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

The paper introduces no new physical or theoretical entities; the shear-gripper and grasp detector are engineered artifacts, not postulated entities.

free parameters (2)
  • SSIM threshold = not reported
    Used in Sec. IV-B step 3 to filter noisy cube faces; chosen by hand and not quantified, affecting pose tracking quality and therefore demonstration data quality.
  • Occlusion threshold = 'less than half' vs 'more than half'
    Sec. VI-A defines occlusion categories with qualitative thresholds; these thresholds affect the reported success rate breakdown and the mapping to prior works.
assumptions (4)
  • domain assumption The fiducial cube tracking pipeline provides sufficiently accurate gripper poses for policy training in dynamic field conditions.
    Validated only in a static environment against ORB-SLAM3 (Sec. IV-B); in the field, accuracy is assumed because the SLAM baseline is unreliable.
  • domain assumption The custom handheld shear-gripper and the robotic end-effector have a minimal embodiment gap, so policies trained on human demonstrations transfer to the robot.
    Sec. IV-A asserts the similarity in design ensures minimal embodiment gap, but no quantitative transfer analysis is provided.
  • domain assumption The 10 demonstration plots and 10 test plots are sufficiently similar that a policy trained on the former generalizes to the latter.
    Sec. V-A describes the split but provides no statistical characterization of pepper variety, plant morphology, or lighting across plots.
  • ad hoc to paper The mapping of 'easy' difficulty to previous works' 'modified peppers' and 'medium/hard' to 'unmodified peppers' is valid for cross-study comparison.
    Sec. VI-C introduces this equivalence to argue comparability with greenhouse results; it is not validated and directly supports the headline claim.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Autonomous Robotic Pepper Harvesting: Imitation Learning in Unstructured Agricultural Environments." pith.science (2026). https://pith.science/paper/3EKD56GV

@misc{pith2026241109929,
  author       = {Pith},
  title        = {Pith review of: Autonomous Robotic Pepper Harvesting: Imitation Learning in Unstructured Agricultural Environments},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3EKD56GV}},
  note         = {Machine review of arXiv:2411.09929}
}
read the original abstract

Automating tasks in outdoor agricultural fields poses significant challenges due to environmental variability, unstructured terrain, and diverse crop characteristics. We present a robotic system for autonomous pepper harvesting designed to operate in these unprotected, complex settings. Utilizing a custom handheld shear-gripper, we collected 300 demonstrations to train a visuomotor policy, enabling the system to adapt to varying field conditions and crop diversity. We achieved a success rate of 28.95% with a cycle time of 31.71 seconds, comparable to existing systems tested under more controlled conditions like greenhouses. Our system demonstrates the feasibility and effectiveness of leveraging imitation learning for automated harvesting in unstructured agricultural environments. This work aims to advance scalable, automated robotic solutions for agriculture in natural settings.

Figures

Figures reproduced from arXiv: 2411.09929 by the authors.

Figure 1
Figure 1. (a) Robotic automation in agriculture faces unique domain-specific [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. (a) Handheld shear-gripper device used for demonstration data [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. The trajectory was derived in a static environment to [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figures from the paper (9 more)
Figure 3
Figure 3. Figure 3: Pipeline for robust cube pose estimation: [PITH_FULL_IMAGE:figures/full_fig_p004_3.png]
Figure 4
Figure 4. Figure 4: The plot shows the 6-DOF pose of the fiducial cube captured during [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: A harvesting demonstration begins with the operator approaching the pepper with the handheld shear-gripper device, followed by cutting and [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: We conducted data collection and experiments at an unprotected [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: Our hardware setup for autonomous pepper harvesting, featuring [PITH_FULL_IMAGE:figures/full_fig_p005_7.png]
Figure 8
Figure 8. Figure 8: Autonomous pepper harvesting by the robot. The approach, cut, grasp, and retraction actions are guided by a trained visuomotor policy learned [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]
Figure 9
Figure 9. Figure 9: Target peppers are categorized into easy, medium, and hard difficulty [PITH_FULL_IMAGE:figures/full_fig_p006_9.png]
Figure 10
Figure 10. Figure 10: (a) Confusion matrix summarizing the grasp detector’s performance. [PITH_FULL_IMAGE:figures/full_fig_p006_10.png]
Figure 11
Figure 11. Figure 11: (a) Various scenarios are encountered, such as instances where five [PITH_FULL_IMAGE:figures/full_fig_p007_11.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

30 extracted references · 24 canonical work pages

  1. [18]

    Performance evaluation of a harvesting robot for sweet pepper,

    C. W. Bac, J. Hemming, B. Van Tuijl, R. Barth, E. Wais, and E. J. van Henten, “Performance evaluation of a harvesting robot for sweet pepper,” Journal of Field Robotics , vol. 34, no. 6, pp. 1123–1139, 2017

    2017

  2. [20]

    Develop- ment of a sweet pepper harvesting robot. j field robot 37: 1027–1039,

    B. Arad, J. Balendonck, R. Barth, O. Ben-Shahar, Y . Edan, T. Hell- str¨om, J. Hemming, P. Kurtser, O. Ringdahl, T. Tielen,et al., “Develop- ment of a sweet pepper harvesting robot. j field robot 37: 1027–1039,” 2020

    work page 2020

  3. [1]

    Recent advancements in agriculture robots: Benefits and challenges,

    C. Cheng, J. Fu, H. Su, and L. Ren, “Recent advancements in agriculture robots: Benefits and challenges,” Machines, vol. 11, no. 1, 2023

    work page 2023

  4. [2]

    Leveraging imitation learning in agricultural robotics: A comprehensive survey and comparative analysis,

    S. Mahmoudi, A. Davar, P. Sohrabipour, R. B. Bist, Y . Tao, and D. Wang, “Leveraging imitation learning in agricultural robotics: A comprehensive survey and comparative analysis,”Frontiers in Robotics and AI, vol. 11, p. 1441312, 2024

    work page 2024

  5. [3]

    Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots,

    C. Chi, Z. Xu, C. Pan, E. Cousineau, B. Burchfiel, S. Feng, R. Tedrake, and S. Song, “Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots,” in Proceedings of Robotics: Science and Systems (RSS) , 2024

    2024

  6. [4]

    Diffusion policy: Visuomotor policy learning via action diffusion,

    C. Chi, S. Feng, Y . Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” in Proceedings of Robotics: Science and Systems (RSS) , 2023

    2023

  7. [5]

    Agricultural robotics for field operations,

    S. Fountas, N. Mylonas, I. Malounas, E. Rodias, C. Hellmann Santos, and E. Pekkeriet, “Agricultural robotics for field operations,” Sensors, vol. 20, no. 9, p. 2672, 2020

    work page 2020

  8. [6]

    3d reconstruction-based seed counting of sorghum panicles for agricul- tural inspection,

    H. Freeman, E. Schneider, C. H. Kim, M. Lee, and G. Kantor, “3d reconstruction-based seed counting of sorghum panicles for agricul- tural inspection,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , pp. 9594–9600, IEEE, 2023

    work page 2023

Show all 30 references
  1. [7]

    Towards autonomous crop monitoring: Inserting sensors in cluttered environments,

    M. Lee, A. Berger, D. Guri, K. Zhang, L. Coffey, G. Kantor, and O. Kroemer, “Towards autonomous crop monitoring: Inserting sensors in cluttered environments,” IEEE Robotics and Automation Letters , vol. 9, no. 6, pp. 5150–5157, 2024

    2024

  2. [8]

    Occlusion reasoning for skeleton extraction of self-occluded tree canopies,

    C. H. Kim and G. Kantor, “Occlusion reasoning for skeleton extraction of self-occluded tree canopies,” in 2023 IEEE International Confer- ence on Robotics and Automation (ICRA) , pp. 9580–9586, 2023

    2023

  3. [9]

    Towards robotic tree manipulation: Leveraging graph representations,

    C. H. Kim, M. Lee, O. Kroemer, and G. Kantor, “Towards robotic tree manipulation: Leveraging graph representations,” in 2024 IEEE Inter- national Conference on Robotics and Automation (ICRA) , pp. 11884– 11890, 2024

    2024

  4. [10]

    Bumblebee: A path towards fully autonomous robotic vine pruning.,

    A. Silwal, F. Yandun, A. K. Nellithimaru, T. Bates, and G. Kantor, “Bumblebee: A path towards fully autonomous robotic vine pruning.,” Field Robotics, vol. 2, no. 1, pp. 1661–1696, 2022

    2022

  5. [11]

    Precision fruit tree pruning using a learned hybrid vision/interaction controller,

    A. You, H. Kolano, N. Parayil, C. Grimm, and J. R. Davidson, “Precision fruit tree pruning using a learned hybrid vision/interaction controller,” in 2022 International Conference on Robotics and Au- tomation (ICRA), pp. 2280–2286, IEEE, 2022

    2022

  6. [12]

    An autonomous strawberry-harvesting robot: Design, development, integration, and field evaluation,

    Y . Xiong, Y . Ge, L. Grimstad, and P. J. From, “An autonomous strawberry-harvesting robot: Design, development, integration, and field evaluation,” Journal of Field Robotics , vol. 37, no. 2, pp. 202– 224, 2020

    2020

  7. [13]

    Development of an autonomous tomato harvesting robot with rotational plucking gripper,

    H. Yaguchi, K. Nagahama, T. Hasegawa, and M. Inaba, “Development of an autonomous tomato harvesting robot with rotational plucking gripper,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. 652–657, 2016

    2016

  8. [14]

    Robotic aubergine harvesting using dual-arm manipula- tion,

    D. Sep ´uLveda, R. Fern ´andez, E. Navas, M. Armada, and P. Gonz ´alez- De-Santos, “Robotic aubergine harvesting using dual-arm manipula- tion,” IEEE Access, vol. 8, pp. 121889–121904, 2020

    2020

  9. [15]

    Hortibot: An adaptive multi-arm system for robotic horticulture of sweet peppers,

    C. Lenz, R. Menon, M. Schreiber, M. P. Jacob, S. Behnke, and M. Bennewitz, “Hortibot: An adaptive multi-arm system for robotic horticulture of sweet peppers,” 2024

    2024

  10. [16]

    Development of an automatic sweet pepper harvesting robot and experimental evaluation,

    Q. Pan, D. Wang, J. Lian, Y . Dong, and C. Qiu, “Development of an automatic sweet pepper harvesting robot and experimental evaluation,” in 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 15811–15817, IEEE, 2024

    2024

  11. [17]

    Sweet pepper pose detection and grasping for automated crop harvesting,

    C. Lehnert, I. Sa, C. McCool, B. Upcroft, and T. Perez, “Sweet pepper pose detection and grasping for automated crop harvesting,” in 2016 IEEE international conference on robotics and automation (ICRA) , pp. 2428–2434, IEEE, 2016

    2016

  12. [19]

    Performance improve- ments of a sweet pepper harvesting robot in protected cropping environments,

    C. Lehnert, C. McCool, I. Sa, and T. Perez, “Performance improve- ments of a sweet pepper harvesting robot in protected cropping environments,” Journal of Field Robotics , vol. 37, no. 7, pp. 1197– 1223, 2020

    2020

  13. [21]

    Imitation learning from a single demonstration leveraging vector quantization for robotic harvesting,

    A. Porichis, M. Inglezou, N. Kegkeroglou, V . Mohan, and P. Chatza- kos, “Imitation learning from a single demonstration leveraging vector quantization for robotic harvesting,” Robotics, vol. 13, no. 7, p. 98, 2024

    2024

  14. [22]

    Plucking motions for tea harvesting robots using probabilistic movement primi- tives,

    K. Motokura, M. Takahashi, M. Ewerton, and J. Peters, “Plucking motions for tea harvesting robots using probabilistic movement primi- tives,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3275– 3282, 2020

    2020

  15. [23]

    Probabilistic movement primitives,

    A. Paraschos, C. Daniel, J. R. Peters, and G. Neumann, “Probabilistic movement primitives,” Advances in neural information processing systems, vol. 26, 2013

    2013

  16. [24]

    Using learning from demonstration (lfd) to perform the complete apple harvesting task,

    R. van de Ven, A. L. Shoushtari, A. Nieuwenhuizen, G. Kootstra, and E. J. van Henten, “Using learning from demonstration (lfd) to perform the complete apple harvesting task,” Computers and Electronics in Agriculture, vol. 224, p. 109195, 2024

    2024

  17. [25]

    Duallqr: Efficient grasping of oscillating apples using task parameter- ized learning from demonstration,

    R. van de Ven, A. Nieuwenhuizen, E. J. van Henten, and G. Kootstra, “Duallqr: Efficient grasping of oscillating apples using task parameter- ized learning from demonstration,” arXiv preprint arXiv:2409.16957 , 2024

    2024 arXiv

  18. [26]

    Robotic handling of compliant food objects by robust learning from demonstration,

    E. Misimi, A. Olofsson, A. Eilertsen, E. R. Øye, and J. R. Mathiassen, “Robotic handling of compliant food objects by robust learning from demonstration,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. 6972–6979, IEEE, 2018

    2018

  19. [27]

    Denoising diffusion probabilistic models,

    J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020

    2020

  20. [28]

    Survey of imitation learning for robotic manipulation,

    B. Fang, S. Jia, D. Guo, M. Xu, S. Wen, and F. Sun, “Survey of imitation learning for robotic manipulation,” International Journal of Intelligent Robotics and Applications , vol. 3, pp. 362–369, 2019

    2019

  21. [29]

    Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam,

    C. Campos, R. Elvira, J. J. G. Rodr ´ıguez, J. M. Montiel, and J. D. Tard ´os, “Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam,” IEEE Transactions on Robotics , vol. 37, no. 6, pp. 1874–1890, 2021

    2021

  22. [30]

    Bridges-2: A platform for rapidly-evolving and data intensive research,

    S. T. Brown, P. Buitrago, E. Hanna, S. Sanielevici, R. Scibek, and N. A. Nystrom, “Bridges-2: A platform for rapidly-evolving and data intensive research,” in Practice and Experience in Advanced Research Computing 2021: Evolution Across All Dimensions, PEARC ’21, (New York, NY...

    2021

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.