{"id":"9e073c99-69ab-4bd6-9ad8-dc005db5b8a5","arxiv_id":"2506.09343","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"A first benchmark that tests whether robots can read multi-page appliance manuals and then plan and execute manipulation tasks on appliances in simulation.","lead":"This paper introduces CheckManual, a benchmark where robots must read appliance manuals to operate microwaves, ovens, and other household appliances in simulation. It provides a new dataset of machine-generated, human-checked manuals and shows that current AI models still perform poorly, so the field has room to improve.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The benchmark's core premise—that LLM-generated, human-revised manuals faithfully represent real manufacturer manuals—is asserted without distributional evidence or inter-annotator agreement, and the same GPT-4o is used on both the generation and evaluation sides.","rationale":"The reader's weakest assumption identifies the same central risk: the synthetic, human-revised manuals may not capture the information content and ambiguity structure of real manufacturer manuals, and no inter-annotator agreement or residual error rate is reported. I agree that this is the most load-bearing point because nearly every other claim—benchmark novelty, difficulty, real-world transfer—depends on the validity of the manual proxy. My read differs in emphasis in two ways. First, I would foreground the absence of any distributional comparison between the generated manuals and the 110 real manuals the authors themselves collected; this is directly checkable and would settle the proxy question. Second, I weight the GPT-4o generation/evaluation confound more heavily as part of the same construct-validity problem: even if the manuals are internally consistent, the benchmark may reward alignment with GPT-4o's generation priors rather than general manual-reading skill. Neither issue is fatal on its own, and both are addressable with additional analysis or data, so the reader's CONDITIONAL verdict remains appropriate. I do not see an internal inconsistency that would justify outright rejection; the dataset, if released with the requested validity checks, could still be a useful resource. The concrete test I propose is the minimal check that would determine whether the proxy concern actually lands.","tokens_in":13019,"tokens_out":4681,"duration_ms":58775,"concrete_test":"Run a distributional comparison between the 110 real manuals collected in §3.1.1 and a random sample of 200 CheckManual manuals. Have independent annotators, blind to source, label each manual for the features enumerated in §3.1.1: part-annotation format (point-line, etc.), operation-guidance strategy (pure text, close-up, decomposition, motion trajectory), and task-expression format (text, bullet list, ordered list, table). Then measure the divergence between the two distributions (e.g., chi-square or KL divergence on feature histograms). If the synthetic manuals are statistically distinguishable from the real manuals on these dimensions, the proxy assumption is weakened; if they are indistinguishable, the concern is resolved.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is that CheckManual is the first benchmark for manual-based appliance manipulation, so success on it should indicate an ability to use real appliance manuals. This requires the synthetic manuals from §3.1 to be a valid proxy for real manufacturer manuals. The paper asserts human revision of 2200+ part annotations, 1400+ tasks, and 1800+ figures (§3.1.2–3.1.4), but it reports no inter-annotator agreement, no residual error rate, and no quantitative comparison between the generated manuals and the 110 real manuals analyzed in §3.1.1. Without such evidence, the claim that human verification 'guarantee[s] correctness' is unsupported. A second issue compounds the risk: GPT-4o is used both to generate the manuals and to implement the ManualPlan planner (§3.1, §5.1). Consequently, high planning success may reflect self-consistency with GPT-4o's own stylistic and content conventions rather than robust, general manual comprehension. If real manuals contain ambiguous diagrams, implicit operation knowledge, or formatting variability that the synthetic pipeline smooths away, then performance on CheckManual may not transfer to the real-world manual-based manipulation that §5.3 gestures toward. This is a construct-validity problem, not a mere evaluation artifact: it determines what the headline numbers mean.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces CheckManual, which it claims is the first benchmark for manual-based appliance manipulation. The authors design an LLM-assisted, human-revised pipeline that starts from PartNet-Mobility CAD models, generates part function annotations, manipulation tasks, and manual figures, and then compiles LaTeX-based PDF manuals. The benchmark provides 1107 manuals for 369 appliances across 11 categories, with 2211 manipulable parts and 1464 manipulation tasks, plus three challenge tracks (manual-CAD-appliance aligned planning, manual-and-CAD-based manipulation, and pure manual-based manipulation) and metrics implemented in the SAPIEN simulator. The authors also propose ManualPlan, a GPT-4o-based high-level planner, and combine it with CAD-assisted primitive actions or VoxPoser for low-level execution. Simulation results show low overall success rates (e.g., 20.70% planning success on Track 1 and 2.39% task success on Track 2), and a short real-robot section reports no quantitative results.","tokens_in":13332,"tokens_out":3561,"duration_ms":40141,"significance":"If the synthetic manuals are a valid proxy for real manufacturer manuals, CheckManual would be a useful and reusable asset: it is the first benchmark of its kind, it is built on articulated CAD models, it provides a large number of human-verified annotations, and it defines concrete evaluation tracks with a simulator environment. The authors also provide an initial baseline family that quantifies the difficulty of the proposed tasks, and the w/o-manual ablations support the intuition that the manual contributes to planning and execution. However, the significance of the benchmark hinges on construct validity: the manuals used for evaluation are generated by the same model family that is evaluated on them, and the paper provides no quantitative evidence that the generated manuals capture the ambiguity, implicit knowledge, and formatting variability of real manufacturer manuals. These issues must be addressed before the headline numbers can be interpreted as evidence of real-world manual-based manipulation ability.","major_comments":[{"comment":"The central claim of the benchmark is that success on CheckManual indicates an ability to use real appliance manuals, but the representativeness of the generated manuals is asserted rather than demonstrated. The analysis of 110 real manuals in §3.1.1 is summarized only in qualitative terms (point-line annotations, four guidance formats, task layout styles), and no quantitative distributional comparison is reported between the real manuals and the 1107 generated manuals. Since the entire benchmark, and the real-world transfer claim in §5.3, depends on the synthetic manuals being a valid proxy, the authors should provide a quantitative comparison of structural and linguistic properties (e.g., annotation style frequencies, task-step distributions, figure types, ambiguity patterns) between real and generated manuals, or otherwise justify the proxy assumption.","section":"§3.1.1–§3.1.5, Fig. 2"},{"comment":"The paper reports that humans verified more than 2200 part annotations, 1400+ tasks, and 1800+ figures, and the introduction states that this verification 'guarantee[s] correctness.' However, no inter-annotator agreement, double-verification protocol, or residual error rate is reported. Without such data, human verification is a described process, not a measured guarantee. The authors should report the verification protocol, the number of annotators, agreement statistics, and the rate of corrected errors; this is especially important for tasks and part states, which directly define the ground truth for the evaluation metrics in §3.3.","section":"§3.1.2–§3.1.4"},{"comment":"GPT-4o is used on both sides of the evaluation: it generates part function names, tasks, and LaTeX manuals in the dataset creation pipeline, and it implements ManualPlan's manual resolution, manipulation planning, and part alignment modules. This is a confound: planning success may reflect GPT-4o parsing its own stylistic and content conventions rather than robust, general manual comprehension. The authors should break this circularity, for example by evaluating ManualPlan with a different planner model on the same manuals, or by evaluating GPT-4o-based planners on a sample of real manufacturer manuals, and comparing the result distributions.","section":"§3.1.2, §3.1.3, §3.1.5 vs. §4.1, §5.1"},{"comment":"All results in Table 2 are point estimates with no variance, number of trials, or number of seeds reported. Given that the key numbers are low (20.70% planning success on Track 1, 2.39% task success on Track 2), and that the w/o-manual ablations show small absolute differences in several categories, the difficulty claim and the importance of the manual need to be supported by repeated runs with standard deviations or confidence intervals. The authors should also specify how many tasks per appliance category and how many independent evaluation runs were used to compute each entry.","section":"Table 2, §5.2"},{"comment":"The step-level success criteria introduce several thresholds — L ± 30° for revolute parts, >25% L movement for buttons and sliders, and the motion-distance ratio >50% — but no sensitivity analysis or justification is provided for these choices. Since these thresholds directly determine the reported success rates, the authors should report how the results in Table 2 change under reasonable variations of these parameters, or at least justify the thresholds against the part sizes and joint limits in the dataset.","section":"§3.3, Evaluation Metrics"}],"minor_comments":[{"comment":"Table 1 contains a duplicated row for 'Track 2: Manual & CAD based Manipulation' and no row for Track 3, which makes the table confusing; the duplication should be removed and the Track 3 row added.","section":"Table 1"},{"comment":"The text uses 'Planing' several times (e.g., 'Track 1: CAD-Appliance Aligned Planing'); this should be corrected to 'Planning'.","section":"§3.3, Table 1"},{"comment":"In the related-work section, 'score a group of kills' appears to be a typo for 'skills' and should be corrected.","section":"§2.1"},{"comment":"The footnote uses 'ChcekManual' instead of 'CheckManual'; the typo should be fixed.","section":"§3.1.1"},{"comment":"The name 'groundingDINO' is written without a space in the implementation details; it should be 'Grounding-DINO' for consistency with the reference list.","section":"§5.1"},{"comment":"The real-robot deployment section reports no quantitative outcomes such as number of tasks attempted, success rates, or failure examples, and the statement that the authors 'follow [11] to cache correct manipulation actions in every step' indicates that the experiment does not evaluate end-to-end manual-based manipulation; either provide full quantitative results without caching or explicitly reword the claim to state that only a qualitative demonstration was performed.","section":"§5.3"}],"recommendation":"major_revision","confidential_remarks":"The core idea is timely, and the dataset, if validated, could be valuable to the manipulation community. The main risk is construct validity: the evaluation corpus and the evaluated planner come from the same model family, and the paper lacks the inter-annotator and distributional evidence needed to support the proxy claim. I would encourage the editor to request a revised version that adds multi-seed results, verification statistics, a non-GPT-4o planner baseline, and a clear statement of what the real-robot experiment does and does not demonstrate."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short take: this paper gives the manipulation community something it didn't have before: a benchmark where a robot must read an appliance manual, align it to a CAD model, and plan/execute manipulation tasks in simulation. That gap was real. TechQA, S10 QA, and PM209 are QA-only; RT-2, OpenVLA, VoxPoser, and friends don't touch manuals. The three tracks sensibly separate planning from execution and CAD-assisted from pure manual-based setting. The dataset is substantial: 1107 manuals for 369 appliances, 2211 parts, 1464 tasks, all human-verified to some degree. The ablation showing that removing the manual drops performance confirms the manual actually carries information. That is genuine, careful work.\n\nNow the soft spots, in order of size. The biggest is construct validity. The manuals are synthetic, generated by GPT-4o and then revised by humans. The paper reports the numbers verified (2200+ parts, 1400+ tasks, 1800+ figures) but no inter-annotator agreement or residual error rate. The claim that success on CheckManual transfers to real-world manual-based manipulation rests on the synthetic manuals being a faithful proxy for real manufacturer manuals. The paper does analyze 110 real manuals to inform the generation pipeline, which is good, but it never shows a distributional comparison between its generated manuals and the real ones. This is not fatal, but it is a load-bearing assumption, and the paper should own it more explicitly.\n\nSecond, the same-model confound: GPT-4o generated the part annotations, tasks, and LaTeX manuals, and the ManualPlan baseline also uses GPT-4o. So the baseline numbers (20.70% planning success on Track 1, 2.39% task success on Track 2) may partially reflect the model parsing its own output style. That doesn't ruin the benchmark, but it weakens the baseline as an honest difficulty estimate, especially since the paper presents ManualPlan as the first manual-based planning model.\n\nThird, there are standard completeness issues: no error bars on Table 2, no explicit data/code release link (only a project page), and the real-robot section is a stub with no quantitative results and cached actions. These are all fixable.\n\nMy bottom line: this is a useful resource that deserves peer review, not a desk reject. The authors did the hard human-annotation work; they just under-validated the synthetic-manual premise. A revision that adds an inter-annotator agreement study or a small human/LLM comparison between generated and real manuals, plus error bars and a public release, would largely resolve my concern. I'd send it out.","headline":"A genuine first for the field—manual-grounded appliance manipulation in simulation—with a substantial dataset, though the synthetic-manual premise and same-model confound need real validation.","tokens_in":763,"tokens_out":1533,"would_cite":true,"duration_ms":35943,"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":"CheckManual is the first benchmark for manual-based appliance manipulation: 1,107 human-verified manuals for 369 articulated appliances, three challenge tracks, and a baseline model ManualPlan that reaches only 20.70% planning success on…","keywords":["manual-based manipulation","appliance manipulation benchmark","robot task planning","multimodal large language model","articulated objects","long-horizon manipulation","simulation benchmark","human-verified dataset"],"falsifier":"Replace the synthetic manuals in Track 1 with real manufacturer manuals for the same appliance categories, have human experts annotate the same task steps, and compare planning success; a large drop would show the synthetic manuals omit information or ambiguities that real manuals contain.","tokens_in":12836,"feed_emoji":"📖","tokens_out":9404,"duration_ms":86632,"temperature":0.7,"pith_summary":"The paper tries to establish that using a household appliance the right way is a skill a robot must learn from the manufacturer's manual, not from common sense, and that this skill can be measured in simulation. To make the test possible, it builds CheckManual: 1,107 human-verified manuals for 369 appliances across 11 categories, with 2,211 labeled movable parts and 1,464 step-annotated manipulation tasks. Its companion planner ManualPlan, the first manual-based manipulation planning model, reaches 20.70% planning success on the easiest track and only 2.39-2.87% task success on the execution tracks, while removing the manual cuts planning success to 7.99%, which the paper takes as evidence that the manual is doing the work. If the benchmark is faithful, it gives the field a concrete way to measure a capability that current large-model planners largely lack.","feed_headline":"Robot planners reach only 20.7% on manual-based appliance tasks","feed_subtitle":"A new benchmark pairs 1,107 CAD-linked manuals with 369 appliances to test whether robots can follow a user manual","key_machinery":"The load-bearing object is the synthetic manual created by a large-model-assisted, human-revised pipeline from articulated CAD appliance models. Each manual is a multi-page PDF in which every movable part receives a function name and a set of function states, tasks are expressed as resolvable step sequences, and figures are drawn in the point-line annotation, close-up, motion-decomposition, and trajectory styles observed in real manuals. The companion ManualPlan model carries the argument by splitting the problem into manual resolution, manipulation planning, and part alignment, so that each of the three challenge tracks can be scored on the sub-capability it isolates.","core_discovery":"The paper's central claim is that manual-based appliance manipulation is a distinct capability, worth benchmarking separately from common-sense tool use and from manual question answering. Each manual in CheckManual is grounded in an articulated CAD appliance, so every named part has a real movable geometry and every task step can be executed and checked in simulation. The proposed ManualPlan baseline shows where the difficulty lies: 20.70% planning success on Track 1, 2.39% task success on Track 2, and 2.87% on Track 3, with the no-manual ablation dropping Track 1 planning to 7.99%. The paper interprets these numbers as showing that reading the manual is the key source of correctness and that all three sub-problems, manual comprehension, part alignment, and multi-step execution, must improve substantially before manual-based appliance manipulation is solved.","pith_inferences":["If the synthetic manuals faithfully reflect real manufacturer manuals, then benchmark scores may predict real-world manual-following ability; the paper does not test that transfer, so it remains open.","A Track 1 variant with perfect perception, scoring only manual comprehension and planning, would isolate reading ability from execution and make error attribution cleaner.","Adding deliberately ambiguous, erroneous, or incomplete manuals could turn CheckManual into a robustness test of whether a model knows when the manual is not enough.","Since human verification is the quality gate for the ground truth, reporting inter-annotator agreement or a residual-error audit would make the benchmark's reliability itself measurable."],"forward_implications":["A model that succeeds on CheckManual must solve manual reading, part grounding, planning, and physical execution together, so the benchmark yields a single comparable score for the full capability.","The gap between the 20.70% planning success on Track 1 and the 2.39% task success on Track 2 quantifies how much is lost to part-alignment and low-level execution errors.","Removing the manual drops Track 1 planning success from 20.70% to 7.99%, isolating the manual as the main source of the benchmark's achievable correctness.","The 1,464 tasks include sequences of up to 18 steps, and success is checked after every step, which makes long-horizon error accumulation a measurable bottleneck.","The simulator environments and metrics let future manual-following manipulation models be compared against these baselines on the same three tracks."],"supporting_citations":[{"why":"Supplies the articulated CAD appliance models that ground every manual, task, and evaluation.","marker":"[22]"},{"why":"The large language model that generates manuals, tasks, and evaluation plans in the pipeline.","marker":"[1]"},{"why":"Provides the simulated part-based environment in which all three challenge tracks are evaluated.","marker":"[28]"},{"why":"The open-vocabulary manipulation model that ManualPlan drives for the CAD-free Track 3 low-level policy.","marker":"[10]"},{"why":"Open-vocabulary detection used to ground movable parts in the part alignment module.","marker":"[18]"},{"why":"Segmentation model that produces part masks for visual prompting and grounding.","marker":"[13]"},{"why":"Pose estimation that aligns the observed appliance with its CAD model for the Track 2 policy.","marker":"[26]"},{"why":"Grasp pose prediction for part-level manipulation primitives in the CAD-assisted policy.","marker":"[7]"},{"why":"Visual prompting mechanism that lets the multimodal model map part masks to manual function names.","marker":"[29]"}],"fun_headline_variants":["New benchmark tests robots that read appliance manuals","Manual-reading robots stumble: 20.7% planning success","Appliance manual benchmark exposes robot planning gap","Robots need manuals: benchmark shows 20.7% planning accuracy"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The benchmark's usefulness rests on the assumption that the LLM-generated, human-revised manuals carry the same information and the same ambiguities as real manufacturer manuals, so success on CheckManual predicts success with real manuals.","fun_headline_variants_meta":{"raw":{"variants":["New benchmark tests robots that read appliance manuals","Manual-reading robots stumble: 20.7% planning success","Appliance manual benchmark exposes robot planning gap","Robots need manuals: benchmark shows 20.7% planning accuracy"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000203,"raw_usage":{"total_tokens":1358,"prompt_tokens":891,"completion_tokens":467,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":507,"completion_tokens_details":{"reasoning_tokens":402}},"tokens_in":507,"tokens_out":467,"duration_ms":4638,"temperature":1.0,"reasoning_tokens":402,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T04:50:52.840485+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Replace the synthetic manuals in Track 1 with real manufacturer manuals for the same appliance categories, have human experts annotate the same task steps, and compare planning success; a large drop would show the synthetic manuals omit information or ambiguities that real manuals contain.","supporting_citations":[{"cited_title":"Chang, Li Yi, Subarna Tripathi, Leonidas J","cited_arxiv_id":null,"evidence_quote":"Supplies the articulated CAD appliance models that ground every manual, task, and evaluation."},{"cited_title":"FoundationPose: Unified 6d pose estimation and tracking of novel objects","cited_arxiv_id":null,"evidence_quote":"Provides the simulated part-based environment in which all three challenge tracks are evaluated."},{"cited_title":"Grounding dino: Marry- ing dino with grounded pre-training for open-set object de- tection, 2024","cited_arxiv_id":null,"evidence_quote":"Open-vocabulary detection used to ground movable parts in the part alignment module."},{"cited_title":"Anygrasp: Robust and efficient grasp perception in spa- tial and temporal domains","cited_arxiv_id":null,"evidence_quote":"Grasp pose prediction for part-level manipulation primitives in the CAD-assisted policy."}],"review_version":1}