{"id":"3e7b1042-b612-42b6-b35b-8d62bd783fa3","arxiv_id":"2607.05869","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":7,"one_line_summary":"GraspIT provides ~316k annotated RGBD frames with ~2.3M slip-test-validated 6-DoF grasp candidates and a bidirectional sim-to-real registration pipeline, all released as open-source Docker containers.","lead":"GraspIT is a robotic grasping dataset of ~316k RGBD frames across 1,135 scenes, annotated with ~2.3M grasp candidates validated by a four-stage physical slip test in simulation. A smart generalist might read it because it combines photorealistic visuals, robot reachability, and graded physics-based grasp scores with a sim-to-real registration loop, filling a gap left by existing datasets.","discovery_kind":"unclear","skeptic_critique":{"model":"glm-5.2","headline":"The slip-test's most discriminating stage (pendulum, filtering 38.84% of candidates) specifically tests off-center mass resistance, yet uniform density (1000 kg/m³) forces center-of-mass to equal geometric centroid — undermining the 'physically validated' label for the stage that matters most.","rationale":"The reader identified the correct load-bearing concern: the uniform physics parameters (µ=1.0, density=1000 kg/m³) undermine the 'physically validated' claim, and no real-robot validation is provided. I agree with this assessment and with the CONDITIONAL verdict.\n\nMy contribution is to sharpen the concern by pointing out that the pendulum stage — which filters 38.84% of all candidates, making it the most discriminating component of the slip-test — specifically tests a property (off-center mass resistance) that is most distorted by the uniform density assumption. This makes the concern not just general ('physics parameters are crude') but structurally targeted: the stage doing the most work in the pipeline is the one most affected by the simplifying assumption.\n\nThe paper has genuine independent support: the dataset is released, tools are open-source and Docker-containerized, the pipeline is described in sufficient detail for reproduction, and the combination of modalities (RGBD + reachability + slip-test + Real↔Sim link) is not available in any cited prior dataset. The contribution as infrastructure is real. The CONDITIONAL verdict is appropriate because the dataset can be independently evaluated, and the concern is about the strength of the 'physically validated' label rather than about internal inconsistency or fabrication.\n\nI do not adjust the verdict because the reader already correctly weighted this concern. The paper is honest about its limitations (§4, items i-vi), which is creditworthy. The concern is addressable through the concrete test proposed above, which requires modest real-robot effort (~250 executions) and would either validate or invalidate the score-to-reality correlation that the dataset's value proposition depends on.","tokens_in":13147,"tokens_out":3337,"duration_ms":185135,"concrete_test":"Select 50 grasps from each score bin (s=0.25, 0.50, 0.75, 1.00) on 10 real objects with known material properties (spanning low-friction metal to high-friction rubber, and varying mass distributions). Execute each grasp 5 times on a real Franka Panda with the Franka Hand gripper (70N closure force, matching §3.5). Compute Spearman rank correlation between slip-test score and real success rate. If the correlation is below 0.5 or if the score bins do not show monotonically increasing success rates, the claim that slip-test scores are more physically meaningful than force-closure is unsupported. This requires only ~250 grasp executions and directly tests the central value proposition.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central differentiator is that its grasp quality scores are 'physically validated' (title, abstract, §3.5), as opposed to force-closure-only datasets like GraspNet-1B. The reader correctly identifies that uniform friction (µ=1.0) and uniform density (1000 kg/m³) for all objects undermine this claim. I want to sharpen this with a specific structural observation.\n\nTable 2 shows the attrition breakdown across slip-test stages. The pendulum swing (stage 4, s=0.75) is by far the most discriminating filter: 883,733 candidates (38.84%) fail here, compared to 1.45% failing lift and 7.36% failing horizontal oscillation. The pendulum test explicitly 'tests resistance to moment arms created by off-center mass' (§3.5, stage 4). But under the uniform density assumption, center-of-mass is always the geometric centroid of the mesh. This means the pendulum test — the stage that does the most work in separating good from bad grasps — is evaluating a property that is most distorted by the uniform density assumption. Real objects with non-uniform mass distribution (hollow, composite, or asymmetrically weighted) would produce systematically different pendulum-test outcomes.\n\nSimilarly, µ=1.0 is a high friction coefficient (comparable to rubber-on-rubber). The lift stage (1.45% failure) and horizontal oscillation stage (7.36% failure) are likely too lenient for low-friction materials like metal or plastic, where real slip would occur at much lower thresholds.\n\nThe paper acknowledges this in limitation (v) but provides no real-robot correlation data. The 82.94% good-grasp rate may be inflated relative to real-world performance, and the score ordering (s=0.25 vs 0.50 vs 0.75 vs 1.00) may not preserve rank correlation with real execution success — which is the property that would make the scores useful for training grasp quality critics. Without even a small-scale real-robot validation showing monotonic correlation between slip-test score bins and real success rates, the 'physi","agreement_with_reader":"agree"},"referee_report":{"model":"glm-5.2","summary":"GraspIT introduces a dataset and generation pipeline for robotic grasping that combines photorealistic RGBD observations, robot-reachability-filtered grasp candidates, a four-stage physical slip-test producing continuous quality scores, and a bidirectional Real↔Sim registration link. The initial release comprises ~316k annotated frames across 1,035 simulated and 100 real scenes, with ~2.3M slip-test-validated grasp candidates (82.94% annotated good, s≥0.50). The slip-test protocol evaluates grasps through trajectory planning, vertical lift, horizontal oscillation, and pendulum swing, producing a graded score with slip penalties (Eq. 2). The pipeline is Docker-containerized and open-source.","tokens_in":13344,"tokens_out":1619,"duration_ms":264102,"significance":"The dataset addresses a genuine gap: no prior dataset simultaneously provides photorealistic RGBD, robot-reachability-filtered grasps, staged physics-validated quality scores, complete 3D shape, and a Real↔Sim registration link (Tables 1, 3). The four-stage slip-test with continuous scoring and explicit slip penalties (Eq. 2) is a thoughtful design that produces graded hard negatives — the 94,475 penalized grasps that pass stages but score below 0.50 are a concrete asset for training grasp-quality critics. The open-source, containerized generation system and the ability to import existing datasets (BOP, GraspNet) into the pipeline enhance community value. The Real↔Sim loop with TRELLIS-based 3D reconstruction and ICP registration is a practical contribution.","major_comments":[{"comment":"§3.5, stage 4 (pendulum swing) and Table 2: The pendulum test is described as testing 'resistance to moment arms created by off-center mass' and is the most discriminating filter (883,733 candidates, 38.84% fail here). However, §3.5 also states all objects use uniform density (1000 kg/m³), which forces center-of-mass to equal the geometric centroid. This means the stage that does the most work in separating good from bad grasps is evaluating a property most distorted by the uniform density assumption. The paper should either (a) provide real-robot validation that pendulum-test outcomes correlate with physical execution for objects with non-uniform mass distribution, or (b) explicitly qualify the 'physically validated' label in the title and abstract to note that validation is under uniform-density assumptions, and discuss the implications for the pendulum stage specifically.","section":null},{"comment":"§3.5 and title/abstract: The central differentiating claim is that slip-test scores are 'physically validated' and more meaningful than force-closure metrics. However, µ=1.0 (comparable to rubber-on-rubber) is used for all object-fingertip interactions regardless of material. The lift stage (1.45% failure) and horizontal oscillation stage (7.36% failure) are likely too lenient for low-friction materials (metal, plastic) where real slip occurs at lower thresholds. Without any real-robot validation showing correlation between slip-test scores and execution success — which the paper acknowledges as absent in limitation (i) — the claim that these scores are more physically grounded than force-closure remains untested. At minimum, a small-scale real-robot validation (even 50-100 grasps across a few object types) would substantially strengthen the central claim. If this is not feasible for the","section":null},{"comment":"§3.5, Eq. (2): The slip-penalty formula uses hand-set constants (2cm positional threshold, 10° orientational threshold, denominator 12). The paper provides a brief calibration rationale (a single violation on a 0.75-base grasp stays marginally good; two violations on a 0.50-base grasp push to bad). However, no sensitivity analysis is provided for how the good/bad classification changes with different threshold or denominator values. Given that 94,475 grasps (4.15%) fall in the penalized bucket — the paper's highlighted 'informative hard negatives' — the stability of this bucket under reasonable parameter perturbations should be reported.","section":null}],"minor_comments":[{"comment":"Table 2: The 'Fail pendulum (s=0.75)' row reports 883,733 (38.84%), but the text in §3.6 states '94.26% survive the pendulum test.' These are consistent (100% - 38.84% ≈ 61.16% fail, not 5.74% fail), so the 94.26% figure appears to be an error or refers to a different denominator. Please clarify.","section":null},{"comment":"Table 2: The 'w/ trajectory' row (2,014,689, 88.54%) and 'No Trajectory to closure (s=0.00)' row (260,663, 11.46%) sum to 2,275,352, which is correct. However, the subsequent stage rows (fail lift, fail horizontal, fail pendulum, full success) should also sum to 2,014,689 (those with trajectories). Adding 33,029 + 94,475 + 167,549 + 883,733 + 835,903 = 2,014,689. This checks out but would benefit from explicit labeling that these rows are conditional on trajectory success.","section":null},{"comment":"§3.4: The UR-5 arm is used for real scene capture, while the Franka Panda is used for grasp validation. This mismatch is not discussed — please clarify whether grasp labels generated for the Franka are intended to transfer to UR-5 setups or whether the real branch is purely for visual data capture.","section":null},{"comment":"Figure 2 caption: 'Robot School' is referenced but not defined until §3.5. A forward reference or brief inline definition would help.","section":null},{"comment":"§3.2: Objects are 'randomly scaled to a maximum side length of [2,20]% of the maximum table side length.' Please clarify whether this means the scaling range is [2%, 20%] or whether 2-20% is applied to some base dimension.","section":null},{"comment":"Table 1: The '≈' symbol for partial provision is used inconsistently across datasets (e.g., GraspNet-1B column D shows '≈' without explanation). A footnote defining the threshold for '≈' vs '✓' would improve clarity.","section":null},{"comment":"§3.5: The Franka Hand closure force is stated as 70N. The default Franka Hand specification is typically ~70N maximum, but please confirm this is the commanded force used in simulation, not the hardware maximum.","section":null},{"comment":"Abstract: 'which existing datasets lack to provide jointly' is grammatically awkward; consider 'which no existing dataset provides jointly.'","section":null},{"comment":"References [2] and [3] (RT-1, RT-2) are cited in the introduction but not included in the reference list with full bibliographic details matching the numbered scheme.","section":null}],"recommendation":"major_revision","confidential_remarks":"The dataset contribution itself is valuable and the pipeline is well-engineered. The core issue is a gap between the strong 'physically validated' framing and the absence of any real-robot validation. The uniform density + uniform friction issue is particularly acute for the pendulum stage, which is the paper's most discriminating filter. If the authors can provide even a small real-robot correlation study or substantially soften the 'physically validated' claim with targeted discussion of the physics limitations, this could move to minor revision. The alternative of re-scoring with varied friction/density parameters for a subset of objects would also address the concern."},"author_rebuttal":{"model":"glm-5.2","summary":"We thank the referee for a thorough and constructive report. The referee correctly identifies that the uniform-density assumption (1000 kg/m³) most distorts the pendulum stage — the stage doing the most filtering work — and that the friction coefficient (μ=1.0) and slip-penalty constants lack sensitivity analysis and real-robot validation. We agree with the substance of all three major comments and will revise accordingly: (1) we will qualify the 'physically validated' language in the title and abstract, add a dedicated discussion of the uniform-density assumption's impact on the pendulum stage, and note real-robot validation as future work; (2) we will add an explicit caveat about the μ=1.0 assumption and its effect on low-friction materials, and scope the 'more physically grounded than force-closure' claim accordingly; (3) we will add a sensitivity analysis for the slip-penalty parameters. A small-scale real-robot validation is a genuinely valuable suggestion that we cannot complete within the revision timeline, and we are transparent about this below.","responses":[{"response":"The referee is correct on both counts. The uniform-density assumption (1000 kg/m³) forces center-of-mass to coincide with the geometric centroid, which directly undermines the pendulum stage's stated purpose of testing resistance to moment arms from off-center mass. We acknowledge that this is the stage doing the most filtering work (38.84% of candidates fail here), and it is precisely the stage most affected by the assumption. We cannot complete option (a) — real-robot validation with non-uniform-mass objects — within the revision timeline. We will therefore implement option (b) in full. Specifically: (1) We will qualify 'physically validated' in the title and abstract to read 'physically validated under uniform-density assumptions' or equivalent language that makes the assumption visible at first encounter. (2) We will add a dedicated paragraph in §3.5 discussing why the pendulum stage is most affected: with uniform density, the moment arm reduces to the geometric offset between the grasp contact line and the centroid, which is still non-trivial for irregular geometries but does not capture real-world cases where mass distribution is non-uniform (e.g., a hollow container, a tool with a heavy handle). (3) We will note in the limitations that the pendulum-stage failure rates should be interpreted as lower bounds on stringency for non-uniform objects — a grasp that fails under uniform density would likely also fail with non-uniform mass, but some grasps that pass may fail in reality. We agree this qualification is essential for honest use of the dataset.","revision_made":"yes","referee_comment":"§3.5, stage 4 (pendulum swing) and Table 2: The pendulum test is described as testing 'resistance to moment arms created by off-center mass' and is the most discriminating filter (883,733 candidates, 38.84% fail here). However, §3.5 also states all objects use uniform density (1000 kg/m³), which forces center-of-mass to equal the geometric centroid. This means the stage that does the most work in separating good from bad grasps is evaluating a property most distorted by the uniform density assumption. The paper should either (a) provide real-robot validation that pendulum-test outcomes correlate with physical execution for objects with non-uniform mass distribution, or (b) explicitly qualify the 'physically validated' label in the title and abstract to note that validation is under uniform-density assumptions, and discuss the implications for the pendulum stage specifically."},{"response":"We agree that μ=1.0 for all material interactions is a significant simplification and that the low failure rates on the lift (1.45%) and horizontal oscillation (7.36%) stages are likely partly attributable to this high friction coefficient. The referee's observation that these stages would be more discriminating for low-friction materials (metal, plastic) is well-founded. We will make the following revisions: (1) We will add an explicit caveat in §3.5 stating that μ=1.0 is comparable to rubber-on-rubber contact and that the lift and oscillation stages are therefore likely too lenient for low-friction materials; this means the good-grasp rate (82.94%) should be interpreted as an upper bound for such objects. (2) We will scope the claim 'more physically grounded than force-closure' to acknowledge that this is a relative claim — the slip test captures failure modes (trajectory infeasibility, slip under perturbation, moment-arm instability) absent from force-closure, but the absolute score values are contingent on the friction and density assumptions. (3) We will strengthen the existing limitation (i) to explicitly state that without real-robot correlation data, the claim that slip-test scores predict real execution success remains unvalidated, and that such validation is the most important next step. Regarding the small-scale real-robot validation (50-100 grasps): we genuinely agree this would substantially strengthen the paper, but we are unable to complete it within the revision timeline due to hardware access constraints. We will state this explicitly as a standing limitation rather than overpromising.","revision_made":"partial","referee_comment":"§3.5 and title/abstract: The central differentiating claim is that slip-test scores are 'physically validated' and more meaningful than force-closure metrics. However, μ=1.0 (comparable to rubber-on-rubber) is used for all object-fingertip interactions regardless of material. The lift stage (1.45% failure) and horizontal oscillation stage (7.36% failure) are likely too lenient for low-friction materials (metal, plastic) where real slip occurs at lower thresholds. Without any real-robot validation showing correlation between slip-test scores and execution success — which the paper acknowledges as absent in limitation (i) — the claim that these scores are more physically grounded than force-closure remains untested. At minimum, a small-scale real-robot validation (even 50-100 grasps across a few object types) would substantially strengthen the central claim. If this is not feasible for the"},{"response":"This is a fair and actionable request. We will add a sensitivity analysis varying the positional threshold (±0.5 cm around 2 cm), the orientational threshold (±2° around 10°), and the denominator (10, 12, 14) and report how the good/bad classification and specifically the penalized bucket (94,475 grasps, 4.15%) change. We expect the penalized bucket to be moderately sensitive to the denominator (since it directly controls how many violations push a grasp below 0.50) and less sensitive to the thresholds (since these control whether a violation is counted at all). We will report the range of penalized-bucket sizes and good-grasp rates under these perturbations and discuss the implications: if the bucket is stable, this strengthens the claim that these are genuinely ambiguous near-misses; if it is unstable, we will note that the bucket boundary should be treated as a soft threshold. This analysis can be computed from the existing slip-test logs without re-running the physics simulation, so it is feasible within the revision timeline.","revision_made":"yes","referee_comment":"§3.5, Eq. (2): The slip-penalty formula uses hand-set constants (2cm positional threshold, 10° orientational threshold, denominator 12). The paper provides a brief calibration rationale (a single violation on a 0.75-base grasp stays marginally good; two violations on a 0.50-base grasp push to bad). However, no sensitivity analysis is provided for how the good/bad classification changes with different threshold or denominator values. Given that 94,475 grasps (4.15%) fall in the penalized bucket — the paper's highlighted 'informative hard negatives' — the stability of this bucket under reasonable parameter perturbations should be reported."}],"tokens_in":13093,"tokens_out":1732,"duration_ms":363955,"standing_objections":["Small-scale real-robot validation (50-100 grasps across object types) cannot be completed within the revision timeline due to hardware access constraints. We agree this is the single most valuable addition the paper could receive and will explicitly flag it as the highest-priority future work item, but we cannot honestly commit to including it in the revised manuscript."]},"desk_editor":{"model":"glm-5.2","letter":"GraspIT is a dataset paper that combines photorealistic RGBD, reachability-filtered grasps, staged slip-test scoring, and a bidirectional sim-to-real registration link — a combination no prior dataset offers. The pipeline is open-source and Docker-containerized, and the dataset statistics are internally consistent. The main weakness is that the physics defaults (uniform friction µ=1.0, uniform density 1000 kg/m³) undermine the claim that the scores are physically validated, especially for the pendulum stage that does most of the filtering.","headline":"GraspIT is a dataset paper that combines photorealistic RGBD, reachability-filtered grasps, staged slip-test scoring, and a sim-to-real registration link — a combination no prior dataset offers. The pipeline is open-source and Docker-containerized, and the dataset statistics are internally consistent. The main weakness is that the physics defaults (uniform friction µ=1.0, uniform density 1000 kg/m","tokens_in":14106,"tokens_out":237,"would_cite":false,"duration_ms":63244,"reading_group":"no","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"glm-5.2","headline":"Dataset gives robots grasps tested by physics, not just geometry","keywords":[],"falsifier":"Train a grasp-quality predictor on GraspIT's slip-test scores and evaluate its correlation with real Franka Panda execution success rates. If the predictor trained on slip-test labels does not significantly outperform one trained on force-closure scores when measured against real-robot grasp success, the central premise that staged physical validation produces more transferable quality labels is unsupported.","tokens_in":13297,"feed_emoji":"🤖","tokens_out":803,"duration_ms":229374,"temperature":0.7,"pith_summary":"The paper introduces GraspIT, a dataset and generation pipeline for robotic grasping that combines photorealistic RGBD imagery with grasp quality scores derived from a four-stage physical slip test in simulation. The central claim is that force-closure analysis—the standard method for scoring grasp candidates—is insufficient, and that staged physical perturbation testing (lift, horizontal oscillation, pendulum swing) produces more informative quality labels, including graded hard negatives that look geometrically valid but fail under dynamic loads. The dataset comprises roughly 2.3 million grasp candidates across 1,035 simulated and 100 real tabletop scenes, with 83% passing the slip test at a good-quality threshold. A bidirectional registration pipeline links real captured scenes to simulation, back-projecting validated grasp labels onto real camera frames so that one physical capture session yields both real sensor data and unlimited synthetic views with consistent annotations.","feed_headline":"Dataset gives robots grasps tested by physics, not just geometry","feed_subtitle":"2.3M grasp candidates scored by a four-stage slip test, with a sim-to-real loop that projects labels onto real sensor frames.","key_machinery":"The four-stage slip test scoring function (Equations 1-2), the cuRobo trajectory planning reachability filter, the Real-Sim bidirectional registration via multi-view ICP, and the Docker-containerized Isaac Sim scene generation pipeline.","core_discovery":"The paper's central object is the four-stage slip test: a sequential physical perturbation protocol (trajectory-to-closure, lift, horizontal oscillation, pendulum swing) executed by parallel simulated Franka Panda arms. This test produces a continuous quality score in five discrete base levels (0, 0.25, 0.50, 0.75, 1.0) with additional slip penalties for positional displacement beyond 2 cm or rotational slip beyond 10 degrees at any executed stage. The key finding is that 17% of grasp candidates that passed force-closure analysis failed the slip test, and 4.15% of candidates nominally passed two or more stages but were penalized below the good threshold due to excessive slip—these constitute","pith_inferences":[],"forward_implications":["Grasp-quality regressors trained on GraspIT's graded hard negatives could learn to anticipate physical failure modes that force-closure scores cannot expose, potentially closing the gap between analytic grasp scoring and real-robot execution success.","The Real-Sim registration loop could be applied to existing datasets like GraspNet or BOP to upgrade their analytic annotations to slip-test-validated labels without recollecting real RGBD observations.","If the slip-test scores transfer to real execution, vision-language-action models pretrained on GraspIT's physically grounded labels could reduce the depth-estimation deficit that current semantic encoders exhibit when processing robotic manipulation scenes.","The open containerized pipeline allows other labs to extend the dataset to new robot kinematic chains, sensor modalities, and object corpora, potentially creating a shared physical-grounding foundation across manipulation research."],"fun_headline_variants":["Grasp dataset filters out 17% of geometry-approved grasps via slip test","Sim-to-real grasp dataset flags force-closure failures with physical slip tests","New dataset scores robotic grasps by physical slip, not just force-closure","Physics slip test rejects 17% of force-closure-approved grasps in new dataset","GraspIT dataset scores grasps through four-stage physical perturbation"],"cache_read_input_tokens":0,"weakest_assumption_plain":"The paper assumes that Isaac Sim's default physics parameters—uniform friction coefficient of 1.0 and uniform object density of 1000 kg/m³ for all objects—produce grasp quality scores that meaningfully transfer to real-world robotic execution. Real objects vary in material, friction, and mass distribution, and the paper provides no real-robot validation that the slip-test scores correlate with physical execution success.","fun_headline_variants_meta":{"raw":{"variants":["Grasp dataset filters out 17% of geometry-approved grasps via slip test","Sim-to-real grasp dataset flags force-closure failures with physical slip tests","New dataset scores robotic grasps by physical slip, not just force-closure","Physics slip test rejects 17% of force-closure-approved grasps in new dataset","GraspIT dataset scores grasps through four-stage physical perturbation"]},"model":"glm-5.2","effort":"high","cost_usd":0.0,"raw_usage":{"total_tokens":1053,"prompt_tokens":578,"completion_tokens":475,"prompt_tokens_details":null},"tokens_in":578,"tokens_out":475,"duration_ms":35013,"temperature":1.0,"reasoning_tokens":402,"cache_read_input_tokens":0,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-08T21:46:30.788113+00:00","model_set":{"reader":"glm-5.2"},"falsifier":"Train a grasp-quality predictor on GraspIT's slip-test scores and evaluate its correlation with real Franka Panda execution success rates. If the predictor trained on slip-test labels does not significantly outperform one trained on force-closure scores when measured against real-robot grasp success, the central premise that staged physical validation produces more transferable quality labels is unsupported.","supporting_citations":[],"review_version":1}