{"id":"dee4e7c4-93f9-49c9-9f33-dd1628736836","arxiv_id":"2502.03072","paper_version":1,"verdict":"REJECT","confidence":"HIGH","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":1,"one_line_summary":"Conditioning a Diffusion Policy on YOLO-detected grasp boxes improved reported success rates in three grasping tasks, but the evaluation gives RoboGrasp a goal prompt the baseline lacks.","lead":"RoboGrasp feeds YOLOv11 grasp-box detections into a diffusion-based robot policy and reports higher success rates than the plain Diffusion Policy on three tabletop grasping tasks. A generalist reader might care because the goal is robot grasping that generalizes to new objects with few demonstrations, but the comparison is unfair and the 'universal' claim is contradicted by the paper's own data.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"PickBig and PickGoods evaluations confound grasp-box conditioning with target-goal specification: RoboGrasp is given a target-identifying grasp box while DP receives no target indicator, so Table 1 gains do not isolate the grasp-affordance mechanism.","rationale":"The paper's central claim is that conditioning a diffusion policy on grasp-box detections yields higher success rates. For that claim to hold, the only difference between RoboGrasp and the DP baseline should be the grasp-box conditioning. Section 4.1 shows this is false for the two headline tasks: in PickBig and PickGoods, the grasp box identifies which object is the target, providing goal-specification information that DP never receives. In PickGoods the authors explicitly state that DP lacks such a prompt, so the 100-vs-0 results cannot be attributed to grasp affordance. This confound is load-bearing because PickBig and PickGoods drive the abstract's 'grasping box prompt tasks' claim and the largest reported gains. The PickCup few-shot results are not confounded by goal ambiguity (one object per trial), but they lack trial counts and error bars, and the class-label input is an additional uncontrolled variable. A controlled test that gives DP the same target-identification box would isolate whether the benefit comes from knowing where to grasp versus where to look. Given the absence of such a control, the evidence does not support the causal claim. The reader's verdict of REJECT is appropriate; no code, data, or detailed protocol is provided to mitigate the concern.","tokens_in":11520,"tokens_out":9554,"duration_ms":82771,"concrete_test":"Add a control condition to PickBig and PickGoods: train a DP+Box baseline that is given the same detector output (a bounding box identifying the target) but without grasp-specific geometry or class labels, holding all other inputs and training identical. If DP+Box matches RoboGrasp's TSR (e.g., ~97.5% on PickBig, ~100% on chocolate/tissue), the gains are from goal specification, not grasp-guidance. If DP+Box remains near DP's original TSR, the grasp-specific conditioning is supported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is that adding grasp-box detections as conditioning inputs causes the performance improvements. The ablations in Section 4.3 claim to compare 'DP Model (Baseline)' versus 'RoboGrasp Model' with all else standardized. However, in PickBig (Section 4.1) the task is to grasp the larger of two blocks; the grasping-box detector outputs a box on the larger block, giving RoboGrasp explicit target identification. DP receives only RGB and state, with no indication of which block is the target. Similarly, PickGoods is explicitly described as incorporating 'a clear, goal-oriented prompt' (Section 4.1): the grasp box identifies the target object among distractors, 'a feature that is notably absent in approaches like DP'. Thus the comparison is not an ablation of grasp geometry but of goal specification. The 30-point TSR gain in PickBig and the 100-vs-0 gains in PickGoods could be achieved by any target-identification cue (e.g., a bounding box around the correct object). The only tasks free of this confound are the PickCup few-shot runs (single object per trial), but even there RoboGrasp receives a semantic class label from the detector (Section 3.2), and success counts, seeds, and error bars are not reported. Therefore Table 1 does not support the claim that grasp-specific affordance, rather than target information, drives the improvements. The paper's own Section 5.2.3 acknowledges fixed object positions and possible 'over-reliance on robot pose priors', further weakening causal attribution.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes RoboGrasp, a diffusion-policy variant that augments RGB and robot-state observations with a grasp-box prediction produced by a fine-tuned YOLOv11 detector. It reports real-robot experiments on three tasks (PickBig, PickCup, PickGoods), comparing RoboGrasp against a standard Diffusion Policy baseline and reporting task success and grasp success rates. The central claims are that grasp-box conditioning improves grasp precision, stability, and generalizability, including few-shot transfer and prompt-conditioned grasping, with success rates up to 34% higher than the baseline.","tokens_in":11799,"tokens_out":8716,"duration_ms":89263,"significance":"If the reported results were valid, the contribution would be practically useful: injecting structured grasp-affordance information into an imitation-learning policy is a simple and potentially low-cost way to improve data efficiency, and the PickCup few-shot results hint at genuine generalization benefits. The paper also makes a reasonable point in Section 5.1 that state-space complexity should be matched by dataset scale. However, the experimental design does not isolate the grasp-affordance mechanism from target/goal specification, and the absence of trial counts, seeds, and error bars prevents any statistical assessment of the quantitative claims. The 'universal grasping policy' framing is also not supported by the included tasks, especially given the failures reported for half of the PickGoods objects. The paper is clearly written and the hardware setup is described in sufficient detail to be reproduced in principle, but the central evidence is presently confounded.","major_comments":[{"comment":"The baseline comparison confounds grasp-box conditioning with goal specification. In PickBig, the grasp box is detected on the larger block and therefore tells RoboGrasp which object is the target, while DP receives no such target indicator. In PickGoods, the paper states that the grasp box is a 'clear, goal-oriented prompt' that identifies the object to pick, 'a feature that is notably absent in approaches like DP.' Consequently, the large TSR gaps in Table 1 (PickBig 97.5 vs 67.5; Meiji chocolate 100 vs 0) could be produced by any target-identification cue, not specifically by grasp-specific affordance information. The conclusion that 'grasping box detections' cause the improvement is not supported by these comparisons.","section":"Section 4.1, Table 1"},{"comment":"No trial counts, seeds, or error bars are reported for any success rate. The table gives percentages to one decimal place without denominators, so the reader cannot determine whether '97.5%' reflects 39/40, 78/80, or some other count, and cannot judge the reliability of differences such as 60 vs 100 in the blue plastic cup few-shot row. The PickGoods rows (Lotus biscuit 0/0, M&M 4/4) are especially in need of raw counts. Without these data, none of the claimed improvements can be statistically evaluated.","section":"Section 4.3.2, Table 1"},{"comment":"The grasp detector validation is not sufficiently described. The paper reports only that 'a representative subset of approximately 500 frames' was manually labeled and that fine-tuning 'achieves a mean Average Precision (mAP) exceeding 98%,' with no train/test split, per-class AP, or number of test frames. Because the detector outputs are used both as training labels for the policy and as inference-time prompts, detector errors propagate directly into the measured policy performance. The authors should report held-out detection metrics and, ideally, an analysis of sensitivity to detector confidence thresholds and box selection.","section":"Section 4.2"},{"comment":"The paper's own limitation statements undercut the 'universal' and 'robust' claims in the abstract and conclusion. Section 5.2.3 acknowledges that the fixed spatial placement of objects in PickGoods 'raises concerns about potential over-reliance on robot pose priors rather than affordance-driven reasoning' and that the policy sometimes prioritizes proximal targets over the prompted object. Moreover, Table 1 shows RoboGrasp failing on two of the four PickGoods objects (Lotus biscuit 0/0, M&M 4/4). The 'universal grasping policy framework' claim should be substantially narrowed, or additional experiments with varied object positions and a broader object set should be provided.","section":"Section 5.2.3, Conclusion"}],"minor_comments":[{"comment":"The abstract's 'up to 34% higher success rates' is not tied to a specific table row or computation; Section 5.2.3 reports a 28.7% PickGoods TSR improvement and Section 5.2.2 a 23.33% GSR improvement. Please define how the 34% figure is obtained.","section":"Abstract, Section 5.2.3"},{"comment":"YOLOv11 is cited to Redmon et al. 2016, which describes the original YOLO architecture; the authors should cite the actual YOLOv11 source. Also, 'Grasp Anything' should be 'Grasp-Anything'.","section":"References, Section 3.1"},{"comment":"The figures show 'average' success rates without error bars, raw counts, or standard deviations; the text should explain how the averages are computed from Table 1 and what variability exists across trials.","section":"Figures 8 and 9"},{"comment":"There are several typographical issues: 'these parameter was considered redundant' should be 'these parameters were considered redundant'; Section 5.2.2 has an unclosed parenthesis after 'see Figure 9'; and Figure 1's caption contains a dangling 'robotic control'.","section":"Section 3, Section 5.2.2"},{"comment":"No code, data, or model checkpoint release is mentioned, which limits reproducibility. At minimum, the trained detector weights and policy configuration should be made available.","section":"General"}],"recommendation":"reject","confidential_remarks":"I agree with the stress-test concern: the reported comparison cannot distinguish grasp-specific affordance information from target/goal specification, and the lack of statistical detail makes the quantitative claims unverifiable. The idea is plausible, and the PickCup results are suggestive, but the current evidence does not support the paper's main claims. A substantially revised study with a goal-equivalent control condition (e.g., a generic bounding box around the target object) and full trial counts would be needed before this could be reconsidered."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: the paper's central comparison is confounded in exactly the way the stress-test says. The idea—feeding a YOLO grasp box into a diffusion policy as extra conditioning—is reasonable and underexplored, and the paper is easy to read, but the experiments as designed cannot support the claim that grasp geometry rather than target identification drives the gains.\n\nWhat's new: integrating a fine-tuned grasp detector with DP, conditioning on class label plus box center, width, and height. That is a legitimate incremental contribution. The three real-robot tasks are sensible, and the paper defines both TSR and GSR and tries to standardize setup across models. It also tells you what it did when training was unstable (doubling PickBig data) and explicitly flags the pose-prior concern in Section 5.2.3. That honesty is worth acknowledging.\n\nThe soft spots are serious. In PickGoods, the paper itself says DP relies only on sensor data 'without explicit goal specification' while RoboGrasp gets a grasp box that identifies the target object among distractors. So this is not an ablation of grasp affordance; it's goal specification versus none. PickBig has the same issue: the box is on the larger block, so RoboGrasp is told which one to pick, DP is not. The gains of 30 points or 100-vs-0 can be explained entirely by the target cue. The only place the affordance idea could show up is the few-shot cups, but there RoboGrasp also receives a semantic class label, and no trial counts or seeds are reported anywhere. The 'universal' claim is undercut by 0% on Lotus biscuit and 4% on M&M. The mAP>98% for the detector is reported without split details, and the auto-labels are used for training, so detector error is silently absorbed into the policy.\n\nAs presented, this is not a reliable result. But the underlying direction is worthwhile. A controlled version would give DP the same target information (e.g., a point or box that only says which object, not how to grasp) and would report per-trial counts, seeds, and error bars. Without that, I would not cite the numbers, and I would not send this to a rigorous venue as is.\n\nI'd desk-reject with a clear revision path. If the authors do the controlled ablation and the effect survives, then it's a solid incremental paper.","headline":"The core comparison is confounded: RoboGrasp is given target-identifying grasp boxes while the DP baseline is not, so the reported gains do not isolate the grasp-affordance mechanism.","tokens_in":12384,"tokens_out":3012,"would_cite":false,"duration_ms":29374,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"RoboGrasp claims that adding detected grasp boxes as conditioning inputs makes a diffusion policy grasp more accurately and generalize better, with task-success gains of up to 33.75 percentage points over the plain Diffusion Policy…","keywords":["robotic grasping","diffusion policy","grasp affordance","few-shot learning","grasping box detection","imitation learning","generalization","promptable grasping"],"falsifier":"Re-run the PickGoods comparison while giving the Diffusion Policy baseline the same target-identifying information RoboGrasp receives—say the object class or a bounding box around the target—but not the grasp geometry. If the baseline's success climbs to RoboGrasp's level, the gain comes from goal specification rather than grasp affordances; if it stays near zero, the grasp box itself is the active ingredient.","tokens_in":11257,"feed_emoji":"🤖","tokens_out":8881,"duration_ms":81280,"temperature":0.7,"pith_summary":"RoboGrasp is an attempt to show that a diffusion-based visuomotor policy becomes markedly better at robotic grasping when it is conditioned on grasp boxes produced by a pretrained detector. The paper compares this augmented policy with the plain Diffusion Policy on three real-robot tasks: choosing the larger of two similar blocks, grasping different cup types with distinct strategies including 5- and 10-demonstration few-shot cases, and following a grasp-box prompt to pick a specified retail item. It reports task-success gains of 30 percentage points on PickBig, perfect scores on both few-shot cup tasks, and a jump from 0 to 100 on the chocolate-bar prompt task. The point of the claim, if it holds, is that explicit visual affordances can be added cheaply to existing diffusion policies to make them robust outside controlled lab settings.","feed_headline":"Grasp-box prompts lift pick success by up to 34 percent","feed_subtitle":"A diffusion policy conditioned on detected grasp regions beats the plain baseline on few-shot and promptable picking.","key_machinery":"The central object is the grasping box, a 2D region on the item marking where it can be grasped, encoded by the centroid coordinates, width, and height; rotation is omitted because the robot arm cannot rotate. A Grasp Detection Module, a YOLOv11-m detector fine-tuned on roughly 500 manually labeled frames, generates these boxes automatically at training time and predicts them at inference. The observation encoder then concatenates the box features with the RGB and robot-state latents into one token per timestep, and the diffusion action head, using a DDIM scheduler with cosine beta noise schedule, denoises actions over 16 steps conditioned on those tokens. The argument is that this affordance channel is what lets the policy focus on the target region instead of relying on implicit patterns in raw sensor data.","core_discovery":"The paper's central claim is that a grasping box—the x-y center, width, and height of the region where an object can be grasped, plus its class label—is a sufficient additional condition for a diffusion policy to pick the right object, use the right grasp strategy, and transfer to objects seen only five or ten times. RoboGrasp is presented as a universal grasping policy framework built on Diffusion Policy: a fine-tuned YOLOv11-m detector supplies the boxes during both training and inference, the observation encoder concatenates them with ResNet34-encoded multi-view RGB and robot state, and the same diffusion action head predicts 16-step actions conditioned on those tokens. The experimental report claims that this explicit spatial guidance makes the policy distinguish the larger of two nearly identical blocks, select handle versus wall versus diameter grasps for different cups, and follow a prompt in a cluttered retail scene, whereas the baseline, lacking explicit goal specification, relies on implicit sensor patterns and fails on those distinctions.","pith_inferences":["Editorial inference: the PickGoods comparison is not a clean test of grasp geometry, because Section 4.1 gives RoboGrasp a goal-oriented prompt that the Diffusion Policy baseline never receives; the chocolate-bar result could in part reflect goal specification rather than the grasp affordance.","Editorial inference: Section 4.2 reports the grasp detector's mAP above 98 percent on an unspecified split of roughly 500 frames, and those same auto-labels train the policy; a held-out label-quality check and an ablation with ground-truth boxes would show whether detector noise or policy conditioning drives the gain.","Editorial inference: the paper's own caveat in Section 5.2.3 that fixed placements in PickGoods may let the policy rely on arm-pose priors limits the prompt-following conclusion; varying object positions would test whether the affordance, not the pose prior, carries the behavior.","Editorial inference: including the omitted rotation parameter and testing on a rotatable arm would extend the same framework to angled grasps, which the current experiments cannot evaluate because the arm is fixed."],"forward_implications":["If the claim holds, adding a grasp detector to an existing diffusion policy is a cheap route to precision gains: no new demonstrations or architecture changes, only an extra conditioning channel.","Few-shot transfer should improve: the paper reports 100 percent task success on a green mug trained with 5 demonstrations and a blue cup with 10, suggesting affordance grounding cuts the data needed for novel objects.","Grasp boxes can act as spatial prompts, so a policy can be told which object to pick in a cluttered scene without language supervision, provided the detector can localize that object.","Because the augmentation is confined to the observation encoder, the same conditioning should transfer to other diffusion-based policies and possibly ACT-style or world-model frameworks, as the paper's future-work section suggests.","The paper's own discussion of PickGoods implies performance depends on scene layout, so gains in simpler two-object scenes may not automatically survive heavy clutter or varied placements."],"supporting_citations":[{"why":"Supplies the Diffusion Policy baseline and the action-head architecture that RoboGrasp augments with grasp-box conditioning.","marker":"Chi et al., 2023"},{"why":"Cited as the YOLO detection architecture underlying the fine-tuned YOLOv11-m grasp detector that generates the boxes.","marker":"Redmon et al., 2016"},{"why":"Provides the DDIM denoising scheduler used in the diffusion action head.","marker":"Nichol & Dhariwal, 2021"},{"why":"Survey that grounds the grasping-affordance representation the paper adds to the policy.","marker":"Kleeberger et al., 2020"}],"fun_headline_variants":["RoboGrasp uses grasp-box prompts to boost pick success 34%","Diffusion policy with grasp boxes masters few-shot picking","Grasp-box conditioning lifts robotic pick rates by 34%","Promptable grasping: RoboGrasp adapts to new objects fast","Universal grasping policy: box prompts beat baselines by 34%"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The whole comparison assumes the only meaningful difference between RoboGrasp and the baseline is the added grasp-box information, and that the automatically generated grasp labels used for training are accurate enough to teach the policy.","fun_headline_variants_meta":{"raw":{"variants":["RoboGrasp uses grasp-box prompts to boost pick success 34%","Diffusion policy with grasp boxes masters few-shot picking","Grasp-box conditioning lifts robotic pick rates by 34%","Promptable grasping: RoboGrasp adapts to new objects fast","Universal grasping policy: box prompts beat baselines by 34%"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000255,"raw_usage":{"total_tokens":1547,"prompt_tokens":894,"completion_tokens":653,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":510,"completion_tokens_details":{"reasoning_tokens":560}},"tokens_in":510,"tokens_out":653,"duration_ms":5736,"temperature":1.0,"reasoning_tokens":560,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-09T05:59:36.325686+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Re-run the PickGoods comparison while giving the Diffusion Policy baseline the same target-identifying information RoboGrasp receives—say the object class or a bounding box around the target—but not the grasp geometry. If the baseline's success climbs to RoboGrasp's level, the gain comes from goal specification rather than grasp affordances; if it stays near zero, the grasp box itself is the active ingredient.","supporting_citations":[],"review_version":1}