{"id":"0b56b6fe-1e32-4934-959e-efb7634af3fe","arxiv_id":"2412.08468","paper_version":3,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":4,"one_line_summary":"Multi-GraspLLM uses a single multimodal LLM, trained on a new 140k-grasp, 1.1M-dialogue dataset, to generate semantic grasp poses for five different robotic hands.","lead":"This paper builds a large synthetic dataset of grasp poses for five robotic hands with language annotations, and trains one multimodal LLM to output grasp angles for any of these hands from a point cloud and an instruction. The dataset and the unified multi-hand model are new, but the evaluation leaves open questions about what the numbers mean and how the results compare to the methods used to generate the data.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The paper's own 'GroundTruth' row in Table 2 shows the raw dataset grasps succeed only ~36% in Isaac Sim, so most training/evaluation labels are infeasible; claims of feasible grasp generation and simulator superiority rest on invalid ground truth.","rationale":"Read in good faith: the paper contributes a new multi-hand dataset and a unified LLM-based architecture, and the real-world part-selection experiments plus cross-hand training ablations are genuine evidence. The load-bearing condition for the central claim is that the dataset labels are valid enough that CD-to-ground-truth and simulator success measure what they claim. Table 2 undercuts this: the 'GroundTruth' row is the raw dataset's own Isaac Sim success (avg 0.36), showing the labels are mostly not successful in the evaluation simulator. Because training and evaluation both use these labels, a model can 'outperform' baselines by learning the output distribution of the same generators while still producing mostly infeasible grasps. The real-world results partly externalize the semantic part of the claim (high Acc), but not feasibility: gripper success is actually below Contact-GraspNet, Allegro success is 0.31 with n=20 and no error bars. The proposed concrete test—filtering to simulator-valid ground truth and recomputing comparisons—would settle whether the apparent gains are real. This is the same weakness the reader identified, sharpened by internal evidence from Table 2; it does not change the CONDITIONAL verdict because the real-world part-selection evidence prevents outright rejection.","tokens_in":18841,"tokens_out":5682,"duration_ms":62437,"concrete_test":"Run Isaac Sim on a random stratified sample of at least 200 Multi-GraspSet test ground-truth grasps (40 per hand) using the paper's own success criterion (stable lift), and compute per-hand success and penetration. Then filter the test set to grasps that pass, recompute Table 2 CD/Pen/Suc for Multi-GraspLLM and the baselines on only the valid subset, and report confidence intervals. If the raw-GT sample success rate is far below 0.5, or if Multi-GraspLLM's margin over baselines shrinks or disappears on physically valid targets, the current simulator comparisons are measuring imitation of infeasible labels rather than grasp quality. This one check separates dataset validity from model validity.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim requires Multi-GraspSet grasps to be physically feasible and semantically appropriate. Construction (Sec. 3.2.1 and A.2) generates poses with DexGraspNet/GraspIt/Contact-GraspNet and filters only with a 2cm penetration threshold; no force-closure or simulator-success filter is applied. The paper's own Table 2 'GroundTruth' row reports the Isaac Sim success rate of these raw labels: average Suc = 0.36, per-hand 0.29–0.44, with average penetration 0.66 cm. Thus roughly two-thirds of the 'ground truth' grasps fail in the same simulator used for evaluation. CD and Suc in Table 2 measure closeness to, and success of, this mostly infeasible distribution; Multi-GraspLLM's Suc = 0.40 against baselines 0.28–0.32 is an improvement, but on a scale where even ground truth is below 0.4. Assertions that the method 'generates feasible' grasps and 'significantly outperforms' in simulation are therefore not yet supported. The real-world part-selection accuracy (Acc 0.82/0.72) is independent support for semantic guidance, but real-world Allegro success is only 0.31, gripper success 0.45 vs Contact-GraspNet 0.48, and no error bars are given. This is an internal-data validity problem, not merely a difference from consensus.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper presents Multi-GraspSet, a large-scale dataset of language-annotated grasps for five robotic hands, and Multi-GraspLLM, a multimodal LLM that takes an object point cloud and a natural-language instruction and outputs grasp-pose bin tokens which are de-tokenized into hand-specific poses. The dataset is built by generating grasps with DexGraspNet, GraspIt, and Contact-GraspNet, filtering only by a 2 cm penetration threshold, annotating contacts via an SDF threshold, and creating about 1.1M conversations with GPT-4o. The model is trained with a two-stage procedure (modality alignment then instruction tuning) and evaluated by Chamfer distance to the dataset labels, penetration depth, Isaac Sim success rate, and real-world trials with a gripper and an Allegro hand. The authors claim state-of-the-art performance and that joint multi-hand training helps.","tokens_in":19128,"tokens_out":7708,"duration_ms":81541,"significance":"If the data and evaluation were valid, this would be a valuable contribution: Multi-GraspSet is, to my knowledge, the first large-scale multi-hand grasp dataset with fine-grained contact annotations and language dialogues, and the unified LLM-based architecture for variable-length hand outputs is a sensible design that enables cross-hand learning. The paper is detailed about dataset construction and training, and the real-world part-selection accuracy is a genuine positive result. However, the quantitative claims of feasibility and of superiority in the simulator and in real grasping success are not currently supported, because the ground-truth labels are shown to be mostly infeasible in the same simulator used for evaluation, and the reported real-world success rates are comparable to baselines with no statistical uncertainty.","major_comments":[{"comment":"The dataset generation filters grasps only by a 2 cm penetration threshold and applies no force-closure or simulator-success filter. Table 2's GroundTruth row shows that these raw labels succeed in Isaac Sim at an average rate of only 0.36, with per-hand values of 0.29-0.44 and average penetration 0.66 cm. Since these labels are both the training targets and the CD reference, the learned model is rewarded for imitating a mostly infeasible output distribution, and the same issue biases every baseline trained on Multi-GraspSet. The claims in the abstract and Section 1 that the method generates 'feasible' grasps and 'significantly outperforms' are therefore not established. I request that the dataset be re-filtered with a physics-based success check and/or force closure, and that training and evaluation be repeated on the filtered subset, or that the results be explicitly framed as imitation accuracy rather than physical feasibility.","section":"Sec. 3.2.1, Sec. A.2, Table 2"},{"comment":"The GroundTruth row in Table 2 is undefined. CD is defined as the distance between predicted and ground-truth hand point clouds, so a row that evaluates the raw dataset against itself should have CD equal to zero by construction. The reported values of 0.65-1.38 imply that some other reference is being used, but that reference is not described. Without a precise definition, this row cannot support the statement in Section 5.3 that the method 'surpasses our original Multi-GraspSet dataset.' Please define what the GroundTruth row compares against and label it accordingly.","section":"Sec. 5.1, Table 2"},{"comment":"The real-world results do not support the claim of 'significantly outperforming' existing methods in grasping success. In Table 10, Multi-GraspLLM achieves gripper success 0.45 versus Contact-GraspNet's 0.48, and Allegro success 0.31 versus 0.29, 0.27, and 0.24 for the three dexterous baselines, with only 20 trials per method and no error bars or confidence intervals. The part-selection accuracy improvements (0.82 vs. 0.42 and 0.72 vs. 0.56) are the clear positive results. Please report trial counts per object, confidence intervals or repeated-seed statistics, and separate the semantic-part-accuracy claim from the physical-success claim.","section":"Sec. 5.3, Table 10"},{"comment":"CD to a single ground-truth hand point cloud is not a direct measure of semantic appropriateness. For a given object part there are many valid grasps, and a semantically correct grasp can have a large hand-point-cloud distance to the single stored label. Table 8's pattern of low-level CD=0.78 versus high-level CD=0.31 may partly reflect underspecification of the instruction rather than a semantic error. Please complement CD with a direct part-selection metric in simulation, such as contact-region overlap with the requested part, or a human evaluation of semantic correctness.","section":"Sec. 5.1, Table 8"}],"minor_comments":[{"comment":"The number of grasp bins is inconsistent: Section 4.2 states '512 discretization bins,' while Section 5.2 says the grasp angles are discretized into 384 bins. Please reconcile these numbers and state which value is used for the main results.","section":"Sec. 4.2 vs. Sec. 5.2"},{"comment":"The contact-determination threshold epsilon in Eq. (1) is never given a numerical value or sensitivity analysis. Please specify epsilon and report how the annotation changes with reasonable variations of this threshold.","section":"Eq. (1), Sec. 3.2.2"},{"comment":"The sentence 'SceneDiffuser, DexGYS and DexGraspNet were evaluated on dexterous hands, both trained on our dataset' is ambiguous because three methods are listed. Please state exactly which baselines were retrained on Multi-GraspSet, which were used with pretrained weights, and give the training details for each retrained baseline.","section":"Sec. 5.3"},{"comment":"DexGYS is reported only for the high-level instruction condition in Table 8, with no low- or mid-level entries. Please clarify whether DexGYS cannot consume such instructions, or whether those evaluations were omitted, and avoid implying a full comparison across conditions.","section":"Table 8"},{"comment":"In Figure 3, the annotation 'Index, ring, ring and thumb finger touch battery of power drill' contains a duplicated 'ring'; this appears to be a typo and should be corrected.","section":"Figure 3"},{"comment":"The real-world evaluation reports 29 objects and 20 tests per method but does not specify how tests are allocated across objects and hands, nor how part-selection accuracy is defined when an object has multiple semantically valid parts. Please provide the full protocol and raw counts.","section":"Sec. C, Table 10"}],"recommendation":"major_revision","confidential_remarks":"The core problem is that the benchmark is built from, and evaluated against, grasps that are themselves mostly unsuccessful in the evaluation simulator, so the main quantitative claims cannot be accepted as-is. However, this is an addressable data-validity issue rather than an unfixable flaw, and the dataset and unified architecture are potentially valuable to the community. I would be willing to re-review after the authors re-validate the dataset, clarify the GroundTruth row, and recalibrate the real-world claims with uncertainty estimates."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"First thing you should know: this paper has a genuinely new dataset, and half of it is solid; the other half is undercut by the paper's own numbers. Multi-GraspSet (five hands, 140k grasps, 1.1M dialogues with contact annotations) fills a real gap. Multi-GraspLLM is a plausible composition of known pieces — PointBERT, Vicuna, discretized action bins, hand-aware de-tokenizer — and the real-world part-selection results (0.82 vs 0.42 for the gripper; 0.72 vs 0.56 for Allegro) are impressive and independently meaningful.\n\nBut here is the load-bearing soft spot. Table 2's GroundTruth row shows the raw dataset grasps succeed only about 36% on average in Isaac Sim (0.29–0.44 per hand). So roughly two-thirds of the 'ground truth' is infeasible in the same simulator used for evaluation. CD and Suc are computed against that distribution. Multi-GraspLLM's Suc=0.40 may beat the baselines, but on a scale where ground truth itself is 0.36, that does not support the abstract's 'significantly outperforms' or the claim of generating feasible grasps. Also, the GroundTruth row's CD should be zero by the paper's own metric definition; it is not, which means either the metric is misdefined or that row means something else. The abstract also overstates the real-world gains: gripper success is actually lower than Contact-GraspNet (0.45 vs 0.48), and Allegro success is only 0.31; the real win is part selection, not grasp stability. No error bars, 20 trials per method.\n\nThe circularity concern is real but partial. The dataset is generated by DexGraspNet/GraspIt/Contact-GraspNet, and variants of those same algorithms are the baselines. However, the real-world part-selection results provide some external grounding, so I would not call the entire evaluation circular.\n\nBottom line: the paper deserves a serious referee. The dataset is a contribution, and the unified cross-hand model is worth testing. But the central feasibility claim is not supported as written. I would recommend major revision: fix the metric definitions, report the fraction of dataset grasps that are simulator-valid, add an Isaac Sim success filter or analyze performance on the feasible subset, add a genuinely unified multi-hand baseline, and release the dataset and code. With those changes, the strong claims might become defensible.","headline":"New multi-hand grasp dataset and a plausible unified model, but the paper's own Table 2 shows most ground-truth grasps fail in simulation, so the feasibility claims do not yet hold.","tokens_in":19752,"tokens_out":2495,"would_cite":false,"duration_ms":24937,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A single multimodal LLM maps a point cloud and a natural-language instruction to feasible, semantically matched grasps for five different robotic hands.","keywords":["multi-hand grasp generation","language-guided grasping","multimodal large language models","grasp dataset","contact annotation","robotic dexterous hands","point cloud language alignment","grasp discretization"],"falsifier":"Take objects and hands whose grasps are collected from human demonstrations or physical robot trials (not from DexGraspNet, GraspIt, or Contact-GraspNet), and measure whether Multi-GraspLLM's predicted grasps match those independent grasps better than per-hand baselines; if its advantage comes from imitating the generators that made its training set, the margin should shrink or vanish.","tokens_in":18614,"feed_emoji":"🤖","tokens_out":4045,"duration_ms":39342,"temperature":0.7,"pith_summary":"The paper tries to show that one language-conditioned model can handle grasp generation for five structurally different robotic hands, where previous systems train a separate model per hand. The supporting claim is a new dataset, Multi-GraspSet, that supplies fine-grained finger-object contact annotations and about 1.1 million natural-language dialogues for 2,100 objects and 140,000 grasp poses. On top of it, Multi-GraspLLM treats grasp angles as discrete \"bin tokens\" and lets an LLM autoregressively predict them from a point cloud and a text instruction. The authors report that the unified model beats single-hand baselines and that joint training across hands improves each hand's performance. If true, this makes semantic grasp generation a single-model, cross-embodiment problem rather than a per-hardware engineering task.","feed_headline":"One model grasps with five different robot hands","feed_subtitle":"Language-guided LLM trained on 140k grasps and 1M dialogues tops per-hand baselines in simulation and on real robots.","key_machinery":"The load-bearing device is the grasp-bin token. Continuous wrist pose and joint angles are uniformly discretized per hand into 384 bins; the LLM outputs these bins as next-token predictions, and a hand-aware linear mapping converts bins back to continuous angles. Point-cloud tokens are aligned to language through a PointBERT encoder and an adaptor, and three special tokens-hand, scale, and grasp bin-let one backbone vary its output format by hand and recover the object's absolute scale.","core_discovery":"Multi-GraspLLM is a single multimodal LLM that maps a point cloud plus natural-language instruction to feasible, semantically appropriate grasp poses for the Allegro, Shadow, Barrett, and Jaco hands and the Panda gripper. The paper argues this works because discretized hand-specific grasp bins function as output tokens in an LLM vocabulary, and because the Multi-GraspSet training data aligns language, geometry, and contact. The reported results show the mixed model outperforms per-hand specialist models and existing grasp generators in simulator metrics (Chamfer distance, penetration, Isaac Sim success rate) and in real-robot part-selection accuracy.","pith_inferences":["The reported advantage is measured against the same auto-generation algorithms used to create the training set, so the real-world gain may partly reflect the model learning the generators' output distribution; a test against independently collected human or physical grasps would separate that.","The LLM's autoregressive decoding of bins may constrain grasp diversity; nothing in the paper measures whether the model covers the dataset's contact-pattern variety rather than collapsing to a few common modes.","The method should extend naturally to more end-effectors, since the only hand-specific pieces are the discretization bounds and the final linear layer; a direct test would be training on a novel hand not in the current five.","The conversation-annotation pipeline uses GPT-4 to paraphrase templates; if those paraphrases drift semantically from the contact annotations, the language-conditioning signal could be noisier than the SDF labels themselves."],"forward_implications":["Deployment of grasp generation becomes a single model: adding a new hand needs a hand-specific discretization bound and linear head, not a separately trained network.","Cross-hand joint training is a source of improvement, not a compromise; the paper's ablations show per-hand quality rises as more hands are added.","Semantic control reaches finger level: instructions that specify which fingers touch which object part change the generated grasp, so language can select functional grasps.","The dataset lets future work train and benchmark semantic multi-hand grasp generation on a common set of 2,100 objects instead of per-hand collections.","Because the test point clouds are disjoint from training, the reported gains concern object generalization rather than memorized meshes."],"supporting_citations":[{"why":"DexGraspNet generates the dexterous-hand grasp poses for Allegro and Shadow used as training data and also serves as a baseline.","marker":"[49]"},{"why":"GraspIt generates the grasp poses for the Jaco and Barrett hands in Multi-GraspSet.","marker":"[28]"},{"why":"Contact-GraspNet generates the Panda gripper grasps and serves as the gripper baseline.","marker":"[41]"},{"why":"DexGYS is the language-guided dexterous grasp baseline trained on the same dataset for comparison.","marker":"[52]"},{"why":"SemGrasp motivates the language-guided grasp formulation and the LLM-assisted conversation annotation approach.","marker":"[18]"},{"why":"GPT-4o is used to generate and refine roughly 1.1 million dialogue samples in the dataset.","marker":"[1]"},{"why":"Vicuna is the LLM backbone that is fine-tuned for grasp-bin prediction.","marker":"[5]"},{"why":"PointBERT is the point-cloud encoder that supplies geometric tokens aligned with language features.","marker":"[60]"},{"why":"OakInk provides 1,800 of the object meshes and their segmentation annotations.","marker":"[58]"}],"fun_headline_variants":["One LLM, five hands, better grasps","Five hands, one model, smarter grasps","Language-guided LLM grasps with five hands","Unified LLM handles five robot hand types"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The whole pipeline treats grasps produced by DexGraspNet, GraspIt, and Contact-GraspNet and filtered only by a 2 cm penetration threshold as correct ground truth for language-conditioned training, so any bias or infeasibility in those auto-generated grasps is inherited by the dataset, the training signal, and the evaluation.","fun_headline_variants_meta":{"raw":{"variants":["One LLM, five hands, better grasps","Five hands, one model, smarter grasps","Language-guided LLM grasps with five hands","Unified LLM handles five robot hand types"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000517,"raw_usage":{"total_tokens":2471,"prompt_tokens":876,"completion_tokens":1595,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":492,"completion_tokens_details":{"reasoning_tokens":1535}},"tokens_in":492,"tokens_out":1595,"duration_ms":12639,"temperature":1.0,"reasoning_tokens":1535,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T17:45:25.386401+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Take objects and hands whose grasps are collected from human demonstrations or physical robot trials (not from DexGraspNet, GraspIt, or Contact-GraspNet), and measure whether Multi-GraspLLM's predicted grasps match those independent grasps better than per-hand baselines; if its advantage comes from imitating the generators that made its training set, the margin should shrink or vanish.","supporting_citations":[{"cited_title":"Oakink: A large-scale knowledge reposi- tory for understanding hand-object interaction","cited_arxiv_id":null,"evidence_quote":"OakInk provides 1,800 of the object meshes and their segmentation annotations."},{"cited_title":"Dexgraspnet: A large- scale robotic dexterous grasp dataset for general objects based on simulation","cited_arxiv_id":null,"evidence_quote":"DexGraspNet generates the dexterous-hand grasp poses for Allegro and Shadow used as training data and also serves as a baseline."},{"cited_title":"Graspit! a versatile simulator for robotic grasping","cited_arxiv_id":null,"evidence_quote":"GraspIt generates the grasp poses for the Jaco and Barrett hands in Multi-GraspSet."},{"cited_title":"Contact-graspnet: Efficient 6-dof grasp gen- eration in cluttered scenes","cited_arxiv_id":null,"evidence_quote":"Contact-GraspNet generates the Panda gripper grasps and serves as the gripper baseline."},{"cited_title":"Semgrasp : Semantic grasp generation via language aligned discretization","cited_arxiv_id":null,"evidence_quote":"SemGrasp motivates the language-guided grasp formulation and the LLM-assisted conversation annotation approach."},{"cited_title":"Gonzalez, Ion Stoica, and Eric P","cited_arxiv_id":null,"evidence_quote":"Vicuna is the LLM backbone that is fine-tuned for grasp-bin prediction."},{"cited_title":"Point-bert: Pre-training 3d point cloud transformers with masked point modeling","cited_arxiv_id":null,"evidence_quote":"PointBERT is the point-cloud encoder that supplies geometric tokens aligned with language features."}],"review_version":1}