Pith. sign in

REVIEW 4 major objections 6 minor 21 references

Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT

T0 review · 4 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read This paper claims that drawing bounding boxes on the camera image—green for the object to pick, red for the destination—is enough visual prompting for a transformer-based imitation policy to perform cluttered pick-and-place with 70–100% suc

desk verdict Central claim is undermined by an internal contradiction: the method section says images are fed without bounding boxes, while Algorithm 1 and Figure 3 show visual prompts as the input; no ablation measures the prompting effect. read the letter →

arxiv 2508.08748 v1 pith:S7Z6JJIV submitted 2025-08-12 cs.RO cs.AI

Muhammad A. Muttaqien , Tomohiro Motoda , Ryo Hanai , Yukiyasu Domae This is my paper
classification cs.ROcs.AI
keywords roboticmanipulationpick-and-placevisualpromptingbounding-boxannotationsActionChunkingwithTransformersimitationlearningretailroboticsattentionheatmaps
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 tries to establish that bounding-box annotations drawn directly on the camera image—green around the item to pick, red around the place location—are sufficient spatial prompts for a transformer-based imitation-learning policy to perform tabletop pick-and-place in cluttered convenience-store-like scenes. The authors pair these prompts with Action Chunking with Transformers (ACT), which predicts smooth chunks of future actions instead of one incremental step, and report success rates of 90% in the simple scenario, 70% initially in harder scenarios, and 90–100% after adding targeted demonstration data. If true, the practical payoff is that a robot can be retargeted to new products and layouts without expensive object models or full scene segmentation; the bottleneck shifts to obtaining accurate annotations and diverse human demonstrations.

What carries the argument

The central object is the combination of annotation-guided visual prompting with Action Chunking with Transformers (ACT): bounding boxes are drawn on the RGB frames, and a transformer encoder–decoder predicts chunks of future joint and gripper actions conditioned on those prompted images, joint states, and a latent style variable. The visual prompt does the perceptual work by marking the target and destination, while ACT does the temporal work by outputting coherent multi-step action sequences instead of step-by-step commands.

What would settle it

Run the trained policy on the same scenes with no bounding boxes drawn, or with boxes shifted by 10–20 pixels, and measure success over a sufficiently large number of trials. If success barely changes, the visual prompt is not doing the claimed causal work; if it collapses, the method requires an external annotation mechanism the paper does not provide.

Watch

Extended reading notes

Core claim

The paper's central claim is that a pick-and-place policy can be driven by minimal visual annotations: a green bounding box marks the object to grasp and a red bounding box marks the destination, and these prompts are overlaid on the RGB image that feeds the ACT transformer. ACT then predicts chunked action sequences—smooth, multi-step motions—rather than per-timestep commands. The authors show this pipeline works across three levels of task complexity using a UR5e arm and a two-finger gripper: in a uniform 3x3 arrangement of boxes the success rate is 90%; with diverse products it initially drops to 70%, but adding 20% more demonstrations for the failure cases raises it to 100%; in a more co

Load-bearing premise

The reported success rates assume that accurate bounding boxes for the object to pick and the place location are available at test time, but the paper provides no automatic detector to supply them; if the boxes must be hand-drawn or are noisy, the claimed performance is unsupported.

Editorial extensions

If this is right

  • If the claim holds, full object segmentation or explicit pose estimation is unnecessary for this class of retail pick-and-place tasks; a simple box overlay is enough spatial guidance.
  • Targeted data collection works as a corrective: adding only 20% more demonstrations for failed cases lifted the complex scenario's success rate to 100%, suggesting failure-informed data is more valuable than uniformly more data.
  • The attention heatmaps imply the learned policy internalizes a pick-then-place itinerary, using the green box as the entry cue and the red box as the goal cue, which explains why chunked action prediction is a natural fit for the task.
  • Object material properties remain the hard boundary of the method: reflective and slippery products need extra demonstrations, and soft flexible objects are excluded entirely, so visual prompting alone does not solve grasp mechanics.

Reading between the lines

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

  • A testable extension the paper leaves implicit is coupling the prompted ACT policy with an automatic object detector that produces the bounding boxes at runtime; the paper assumes these annotations are given, so end-to-end performance under imperfect detections is unknown.
  • A reader could probe the causal role of the visual prompt by shifting or jittering the boxes at test time; if success does not drop, the policy may be relying on scene layout rather than the annotations, and if it does drop, the method's real-world utility hinges on annotation accuracy.
  • The box dimensions themselves could carry unused information: since the paper treats boxes as location cues only, training with boxes that also encode object size or approach direction might improve grasp selection without any architectural change.
  • The data-demands acknowledged in the conclusion suggest the main bottleneck is human demonstration cost, so a synthetic data-augmentation pipeline that mimics human trajectories could be the highest-value next step.
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 / 6 minor

Summary. The paper proposes an imitation-learning pipeline for robotic pick-and-place in retail settings, combining Action Chunking with Transformers (ACT) with annotation-guided visual prompting: bounding boxes identify the target object and the destination, and the prompted RGB image is fed into the ACT encoder. The authors evaluate the approach in three scenarios of increasing complexity using five product types, reporting success rates between 70% and 100% and presenting attention heatmaps and failure-mode visualizations. The central claim is that this annotation-guided visual prompting improves grasp accuracy and adaptability compared to conventional ACT-based pick-and-place.

Significance. If the central claim were supported, the contribution would be practically useful: bounding-box prompting is a lightweight way to inject task-level spatial information into an imitation-learning policy without retraining on new objects, and the progressive-scenario evaluation is a reasonable way to stress-test the idea. The paper also builds on the public RoboManipBaselines framework and the existing ACT algorithm, and it provides qualitative attention and failure analyses. However, the evidence as presented does not substantiate the claimed improvement: there is no comparison with ACT without visual prompts, the method description is internally inconsistent about whether annotations are used at inference, and the reported success rates are statistically fragile and in places inconsistent with the accompanying table.

major comments (4)
  1. [Section V.D, Table I] The method description is internally contradictory. Algorithm 1 (line 8) and Fig. 3 construct Ivp_t by adding visual prompts to the RGB image and use it as the ACT encoder input; Fig. 4 shows green/red bounding boxes as the task specification. Yet Section III.C states that captured images are 'directly fed into ACT's CNN network ... without explicit bounding box annotations.' If annotations are not available at inference, then Table I does not demonstrate any effect of visual prompting; if they are available, the paper must say how they are obtained — no automatic detector is proposed or evaluated, and there is no ablation for noisy or missing boxes. This is a load-bearing gap for the paper's main claim and must be resolved with a with-prompt vs. without-prompt comparison and a clear inference-time annotation protocol.
  2. [Section V.D] The reported numbers do not consistently match the table. In Scenario 2, the text says the success rate 'initially dropped to 70%' and later 'achieving a 100% success rate,' but Table I lists category-level rates of 70%, 0%, 100%, and 0% (average 42.5%) for the initial setting and 90%, 100%, 100%, and 100% (average 97.5%) for the more-data setting. Similarly, in Scenario 3 the text says the success rate 'initially decreased to 70%,' while the table's average is 85%. These discrepancies need to be reconciled, and the number of trials per category and per product should be stated explicitly.
  3. [Section V.D] There is no baseline condition: the experiments never run ACT without visual prompting under the same task definitions, data collection, and evaluation protocol. The abstract and conclusion claim 'improved grasp accuracy and adaptability,' but 'improved' is not measured against any control. At minimum, the paper needs an ACT-only ablation with raw RGB images, and ideally a comparison to a standard ACT policy or a non-prompted policy with the same demonstration budget. Without this, the reported success rates cannot be attributed to the annotation mechanism.
  4. [Section III, Section V.C] It is unclear whether the results in Table I come from the real UR5e/Robotiq setup or from simulation. Section III says 'Both real-world and simulation experiments were conducted using the RoboManipBaselines framework,' but Section V.C describes simulation (MuJoCo) only for hyperparameter tuning, and Section V.D does not specify the evaluation environment. This distinction is essential for interpreting the success rates and for reproducibility; the authors should state per experiment whether it was real or simulated and describe the evaluation protocol accordingly.
minor comments (6)
  1. [Section III.D] Typo: 'handle and diverse product types' should likely be 'handle diverse product types.'
  2. [Table I] The category 'Rigid 1 (Choco Box, Biscuit Box)' groups two distinct products, but the text says 'Each product was tested with 10 trials.' Please clarify whether the success rate is per product or per category, and report the denominator for each row.
  3. [Algorithm 1] The visual-prompt rendering is underspecified: line width, color, coordinate frame, and whether prompts are overlayed on the 480x640 image or resized are not given. This affects reproducibility.
  4. [Fig. 5] The attention heatmaps are described only qualitatively; the method for extracting and aggregating them across encoder layers is not stated. Please specify how the heatmaps are generated.
  5. [References] Reference [19] (Dosovitskiy et al., 2014) appears not to be cited in the text; either cite it where relevant or remove it.
  6. [Section V.D] Phrases like 'before adding more data' are vague; please give the actual demonstration counts for the initial and augmented datasets in both Scenario 2 and Scenario 3.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found; the paper is an empirical system study. The internal inconsistency between test-time 'without explicit bounding box annotations' and the training-time use of visual prompts is a missing-control/correctness concern, not a circularity.

full rationale

This is an empirical robotics paper, not a derivation. Success rates in Table I are measured outcomes, not quantities derived from fitted parameters. The central method uses Action Chunking with Transformers (ACT), which is cited from external prior work [11], and ACT is trained and evaluated in a standard supervised imitation-learning setup. The visual-prompt contribution is a conditional empirical claim: when bounding-box prompts are supplied during training and at inference, the ACT policy achieves the reported success rates. No equation in the paper defines a predicted quantity as equivalent to an input by construction, and no fitted parameter is renamed as a prediction. The only self-citation is [15], RoboManipBaselines, a software framework used to run experiments; it is a tool, not a load-bearing mathematical or empirical premise for the central claim. The paper does contain a genuine methodological inconsistency: Section III.C states that captured images are 'directly fed into ACT's CNN network ... without explicit bounding box annotations,' while Algorithm 1 (line 8) and Figure 3 construct and input Ivp_t (images with visual prompts). This inconsistency, together with the absence of a with-prompt vs. without-prompt ablation, means the results do not cleanly establish that the bounding-box prompts are what drives performance. However, that is a missing-control / attributable-variance issue, not a circular reduction. The conclusion's stated limitation about being 'data-demanding' is likewise a practical limitation, not a circular step. Therefore, under the rule that circularity must be shown by quoting a specific reduction or fitted-input-as-prediction step, no significant circularity is present.

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

No new physical entities are postulated. The main burden is on the annotation assumption and the small-sample evaluation; hyperparameters are tuned but not fully reported.

free parameters (3)
  • batch_size = not specified (range 8 to 2)
    Tuned in simulation, but the final value is not stated in the paper.
  • chunk_size = 100
    Selected from a range of 100 to 140; the choice is justified only by a vague tradeoff.
  • epoch_count = 1600
    Selected from a range of 1000 to 1600; reported to be important for success rate but no ablation is shown.
assumptions (3)
  • domain assumption Bounding box annotations provide sufficient spatial guidance for a learned policy to perform pick-and-place.
    This is the core of the method, but it is never tested against a no-prompt baseline or noisy annotations.
  • domain assumption ACT trained on teleoperated demonstrations generalizes to the evaluation layouts.
    Standard imitation learning assumption, but only 10 demonstrations per product are collected, which is a small sample.
  • domain assumption Success rate over 10 trials per product is a stable estimate of performance.
    No variance, confidence intervals, or repeated runs are reported, so the 10-trial estimate is fragile.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT." pith.science (2026). https://pith.science/paper/S7Z6JJIV

@misc{pith2026250808748,
  author       = {Pith},
  title        = {Pith review of: Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S7Z6JJIV}},
  note         = {Machine review of arXiv:2508.08748}
}
read the original abstract

Robotic pick-and-place tasks in convenience stores pose challenges due to dense object arrangements, occlusions, and variations in object properties such as color, shape, size, and texture. These factors complicate trajectory planning and grasping. This paper introduces a perception-action pipeline leveraging annotation-guided visual prompting, where bounding box annotations identify both pickable objects and placement locations, providing structured spatial guidance. Instead of traditional step-by-step planning, we employ Action Chunking with Transformers (ACT) as an imitation learning algorithm, enabling the robotic arm to predict chunked action sequences from human demonstrations. This facilitates smooth, adaptive, and data-driven pick-and-place operations. We evaluate our system based on success rate and visual analysis of grasping behavior, demonstrating improved grasp accuracy and adaptability in retail environments.

Figures

Figures reproduced from arXiv: 2508.08748 by the authors.

Figure 1
Figure 1. The perception-to-action system for robotic manipulation is trained using an imitation learning algorithm (ACT) and [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. A snapshot of the manipulator arm (UR5e), a [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Architecture of the proposed model (ACT) with visual prompting during training. The RGB image is augmented with [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: The bird’s-eye view of the experimental setup shows [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Attention heatmaps are used to assess where ACT focuses when processing bounding box prompts for performing picking [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Different products to pick and different locations to [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

21 extracted references · 19 canonical work pages

  1. [1]

    Saxena, A., Driemeyer, J., Kearns, J., & Ng, A. Y . (2007). ”Robotic Grasping of Novel Objects.” Advances in Neural Information Processing Systems 19 (NIPS 2006)

    work page 2007

  2. [2]

    Collet, A., Martinez, M., & Srinivasa, S. (2011). ”The MOPED Frame- work: Object Recognition and Pose Estimation for Manipulation.” The International Journal of Robotics Research , 30(10), 1284-1306

    work page 2011

  3. [3]

    Zhang, H., Tang, J., Sun, S., & Lan, X. (2022). ”Robotic Grasping from Classical to Modern: A Survey.” arXiv preprint arXiv:2202.03631

    arXiv 2022

  4. [4]

    Wang, R., Zhang, J., et al. (2023). ”DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation.”Proc. IEEE Int. Conf. Robot. Autom. (ICRA)

    work page 2023

  5. [5]

    Jaskowski, M., Swiatkowski, J., et al. (2020). ”Improved GQ-CNN: Deep Learning Model for Planning Robust Grasps.” arXiv preprint arXiv:1802.05992

    work page Pith review arXiv 2020

  6. [6]

    Leeper, A., Hsiao, K., et al. (2012). ”Strategies for Human-in-the- Loop Robotic Grasping.” In Proceedings of the 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    work page 2012

  7. [7]

    Zeng, A., Song, S., et al. (2018). ”Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning.” In Proceedings of the 2018 IEEE/RSJ International Conference on Intelli- gent Robots and Systems (IROS)

    work page 2018

  8. [8]

    Shridhar, M., Manuelli, L., & Fox, D. (2021). ”CLIPort: What and Where Pathways for Robotic Manipulation.” In Proceedings of the 5th Conference on Robot Learning (CoRL)

    work page 2021

Show all 21 references
  1. [9]

    Nasiriany, S., Xia, F., et al. (2024). ”PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs.” arXiv preprint arXiv:2402.07872

    2024 arXiv

  2. [10]

    Fang, K., Liu, F., Abbeel, P., & Levine, S. (2024). MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting. Robotics: Science and Systems (RSS)

    2024

  3. [11]

    Z., Kumar, V ., et al

    Zhao, T. Z., Kumar, V ., et al. (2023). ”Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware.” In Proceedings of Robotics: Science and Systems (RSS)

    2023

  4. [12]

    Kopicki, M., et al. (2016). ”Probabilistic Grasp Transfer for Robotic Manipulation.” IEEE Transactions on Robotics , 32(1), 81-94

    2016

  5. [13]

    Levine, S., Pastor, P., Krizhevsky, A., & Quillen, D. (2018). ”Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection.” The International Journal of Robotics Research, 37(4–5), 421–436

    2018

  6. [14]

    Mahler, J., Liang, J., et al. (2017). ”Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics.” In Proceedings of the 13th Robotics: Science and Systems (RSS)

    2017

  7. [15]

    Murooka, M., Motoda, T., & Nakajo, R. (2024). RoboManipBase- lines (Version 1.0.0) [Software]. Available at: https://github.com/isri-aist/ RoboManipBaselines

    2024

  8. [16]

    Quigley, M., Conley, K., et al. (2009). ”ROS: An Open-Source Robot Operating System.” In ICRA Workshop on Open Source Software

    2009

  9. [17]

    Hochreiter, S., & Schmidhuber, J. (1997). ”Long Short-Term Memory.” Neural Computation, 9(8), 1735-1780

    1997

  10. [18]

    Vaswani, A., Shazeer, N., et al. (2017). ”Attention Is All You Need.” In Advances in Neural Information Processing Systems , 30 (NeurIPS)

    2017

  11. [19]

    T., et al

    Dosovitskiy, A., Springenberg, J. T., et al. (2014). ”Discriminative Un- supervised Feature Learning with Convolutional Neural Networks.” In Advances in Neural Information Processing Systems , 27 (NeurIPS)

    2014

  12. [20]

    Todorov, E., Erez, T., & Tassa, Y . (2012). ”Mujoco: A Physics Engine for Model-Based Control.” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) , 5026-5033

    2012

  13. [21]

    Paszke, A., Gross, S., Massa, F., et al. (2019). ”PyTorch: An Imperative Style, High-Performance Deep Learning Library.” Advances in Neural Information Processing Systems (NeurIPS) , 32

    2019

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.