{"id":"204b6314-e299-4941-935a-e27f629ac137","arxiv_id":"2412.05789","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"InfiniteWorld presents an Isaac Sim based simulator with unified assets and four benchmarks, including scene graph exploration and social mobile manipulation, but reports zero success on the main social task.","lead":"InfiniteWorld is a robot simulation platform built on Nvidia Isaac Sim that merges existing 3D asset generation and reconstruction tools into one framework and adds four embodied AI benchmarks. The authors hope that unifying assets and benchmarks will speed up robot learning, but the paper's flagship social interaction benchmark still defeats all tested methods.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The OWSMM benchmark cannot yet support the paper's central evaluation claim: all success rates in Table 5 are zero, and Sec 4.5 attributes this to the coarse semantic maps from Benchmark 3, so the benchmark has not been shown to measure social interaction ability.","rationale":"The reader's conditional verdict already captures the decisive weakness. The paper presents OWSMM as a key contribution, but Table 5 shows zero success across all conditions, and Sec 4.5 explicitly attributes this to the coarse semantic maps inherited from Benchmark 3. That is an internal admission that the benchmark, as reported, may not measure social interaction ability at all: if target objects are absent from the constructed maps or their parsed positions are far from ground truth, task success is impossible regardless of the agent's social reasoning or planning quality. This is not a demand for high baseline scores; hard benchmarks can be valuable. The problem is that the authors themselves explain the zeroes by a task-level artifact, so the inference from '0% success' to 'agent lacks social interaction capability' is broken. The proposed oracle-map rerun would settle whether the benchmark design is salvageable or whether the zero result is purely an artifact of the mapping pipeline. I also note that no code or data release is provided, so Table 5 cannot be independently recomputed; nevertheless, the central concern is the internal inconsistency in the evaluation logic, not the absence of external verification. The rest of the framework, including asset construction, Real2Sim, and annotation, may have engineering value, but the paper's evaluation claim is not yet supported. The reader's conditional verdict remains appropriate, so I recommend no change.","tokens_in":18074,"tokens_out":3744,"duration_ms":40461,"concrete_test":"Fix the exploration and mapping stage: rerun the OWSMM conditions in Table 5 using the simulator's ground-truth scene graph, or an oracle semantic map, in place of the Benchmark-3 maps, while keeping the same task prompts, robot interfaces, and VLM/LLM methods. Report SR and SPL, plus a per-failure breakdown (map missing object, wrong position, planning failure, manipulation failure) over a fixed set of, say, 100 generated tasks. If SR remains 0, the benchmark's action interface or task design is unsolvable and OWSMM cannot evaluate social interaction; if SR becomes nonzero, the original Table 5 zeroes are artifacts of the coarse mapping pipeline. Either outcome determines whether the benchmark claim survives as stated.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central claim that InfiniteWorld provides 'generalized free robot interaction benchmarks' and a comprehensive evaluation rests on Benchmark 4 (OWSMM, Sec 4.1). For that benchmark to be a valid test of social mobile manipulation, task success must depend on the agent's perception, planning, and social interaction, not on an irreparable upstream artifact. Table 5 reports 0.00 success rate for every OWSMM condition, hierarchical and horizontal, with and without action primitives. The authors' own analysis in Sec 4.5 says the cause is that the maps carried over from Benchmark 3 were built from semantic information that was 'often too coarse', so 'the object instances corresponding to the tasks might not have appeared in the constructed maps, or the parsed positions had large discrepancies from the actual locations.' Under those conditions, no planner, whatever its social reasoning quality, can succeed, and the zero result cannot be interpreted as an evaluation of social interaction. The paper's conclusion that the platform 'comprehensively evaluate[s]' embodied agents is therefore not supported for the flagship benchmark. This is an internal-consistency concern: the manuscript itself flags the missing support. The same issue also weakens the transfer from Benchmark 3 to Benchmark 4, since the administrator's knowledge in hierarchical interaction is built from these same coarse maps.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper presents InfiniteWorld, a robotics simulation framework built on NVIDIA Isaac Sim, with four pillars: generative 3D asset construction (HOLODECK-based scene generation, TripoSR-based image-to-object reconstruction, CAGE articulation generation), a \"depth-prior-constrained\" Real2Sim pipeline built on PGSR, an AI-assisted point-cloud annotation tool (Annot8-3D), and a unified .usd asset interface over existing scene and object datasets. On the task side, the paper defines four benchmarks: object loco-navigation, loco-manipulation, scene-graph collaborative exploration (SGCE), and open-world social mobile manipulation (OWSMM) with hierarchical and horizontal interaction variants. Experiments with a Stretch robot show high success for LLM-based instruction following on the two navigation/manipulation benchmarks when scene semantics and built-in planning interfaces are supplied, near-zero success for VLM zero-shot agents, moderate exploration rates (roughly 25-32% SER) for SGCE, and 0% success in all OWSMM conditions, which the authors attribute in Section 4.5 to coarse semantic maps carried over from Benchmark 3.","tokens_in":18486,"tokens_out":12758,"duration_ms":123351,"significance":"The paper addresses a genuine community need, the fragmentation of simulation assets and interfaces, and packages a large amount of integration work into one platform; if released and validated, the SGCE and OWSMM task designs, particularly the distinction between hierarchical (administrator) and horizontal (peer) knowledge exchange, would be a useful addition to embodied-AI benchmarking. Credit is due for reporting the all-zero OWSMM results honestly rather than tuning the protocol until favorable numbers appear. However, the flagship OWSMM benchmark currently fails for reasons orthogonal to social interaction, no oracle or successful condition demonstrates that OWSMM tasks are solvable, the Real2Sim improvement is supported only qualitatively, and none of the core artifacts (code, scenes, assets, benchmark instances) is released; the significance is therefore conditional on substantial additional validation and on a full release.","major_comments":[{"comment":"All three OWSMM conditions report 0.00 success rate, and the manuscript itself locates the cause in the upstream mapping pipeline: Section 4.5 states that the Benchmark-3 maps were built from semantic information \"often too coarse\", so task objects were missing or misplaced in the maps. Under these conditions the zero results measure map quality, not social interaction ability, and because none of the three reported conditions actually exercises the administrator Q&A or the horizontal dialogue described in Section 4.1, the claimed evaluation of \"communication\" is unsupported. The paper also provides no oracle, scripted, or ground-truth-map condition showing that the OWSMM tasks are solvable at all, and it does not state which SGCE method produced the maps used in OWSMM. The benchmark therefore cannot yet support the paper's central claim of a comprehensive evaluation; this requires either a working exploration-to-map-to-manipulation pipeline with a positive control, or a re-scoping of the contribution.","section":"Section 4.1, Section 4.5, Table 5"},{"comment":"In every LLM-based row of Tables 2 and 3, SPL equals SR exactly (for example, 90.82 = 90.82 and 77.28 = 77.28), which implies that every successful episode achieved exactly the optimal path ratio of 1.0. Combined with the task-generation setting that combines HSSD scene semantics with built-in occupancy maps, D* Lite path following, and adhesion interfaces, this indicates that the LLM-based condition is executing a scripted, oracle-informed plan rather than performing perception-guided navigation; the comparison against the observation-only VLM zero-shot condition is therefore not a like-for-like evaluation of embodied agents. The paper should state precisely what information the LLM receives (in particular whether object coordinates are ground truth), and it should report episode counts, scene splits, and error bars, none of which are given for any table.","section":"Section 4.5, Tables 2-3"},{"comment":"The claimed improvement to Real2Sim, adding Depth-Pro depth estimation and planar normal regularization to PGSR, is supported only by qualitative image comparisons in Figure 2; no quantitative reconstruction metrics (such as Chamfer distance, F-score, or PSNR) and no ablations isolating the two added loss terms are reported, and no loss formulation appears in the main text. Because the paper presents this as an \"improved\" pipeline, the claim is currently unverifiable; it should be backed by numbers and an ablation, or the wording should be softened to describe an adapted pipeline.","section":"Section 3.2, Figure 2, Section 6.1"},{"comment":"The SGCE results show very weak discrimination: the best method (Co-NavGPT with GPT-4) reaches SER 0.3209 against 0.3030 for Random and 0.2581 for the single semantic map, a gap that is meaningless without variance estimates or a significance test, and the reported MRMSE values of 5.78-7.74 m are far too large to ground the object positions needed by the downstream OWSMM tasks, which the paper itself concedes in Section 4.5. The benchmark protocol should specify the number of scenes, episodes, seeds, number of agents, and communication settings, and it should include a positive control verifying that the generated scene graphs are accurate enough to support subsequent task execution.","section":"Section 4.1 Benchmark 3, Table 4"},{"comment":"The central deliverable is not available for scrutiny: the repository link is a placeholder, the claimed 10K/2.36M generated scenes and the unified asset conversions are described only by counts with no release, and the manuscript states that the constructed scenarios \"will be published upon acceptance\". For a platform and benchmark paper, verification of the central claims requires at minimum a full release of code and assets, or a detailed protocol (task instance lists, scene splits, episode counts, seeds) sufficient for independent reimplementation; as it stands, the reader cannot check that the simulator, the asset interfaces, or the benchmarks exist as described.","section":"Section 3.1, Section 3.4, Abstract, GitHub link"}],"minor_comments":[{"comment":"Reference [55] cites REBOUND, an N-body simulation code, but Table 6 compares an annotation tool named ReBound; the citation is mismatched and should be corrected.","section":"Reference [55], Table 6"},{"comment":"The paper says scene count \"can be easily expanded 236 times\" through 236 floor and wall textures, while the abstract says \"200+ different scene style changes\"; the relationship between these numbers, the 10K scenes, and the claimed 2.36M total should be stated precisely and consistently.","section":"Section 3.1"},{"comment":"The OWSMM metrics MPL and LPL are not defined (what constitutes an action path, and how are minimum and longest action paths computed), and the value MPL = 0.00 for the \"VLM Explore+Act Prim\" row is unexplained.","section":"Section 4.4, Table 5"},{"comment":"Tables 7 and 8 report counts of the source datasets (Objaverse, HSSD, HM3D, etc.) rather than the number of assets actually converted to .usd, validated, and made interactive in Isaac Sim; the size of the implemented unified asset library should be quantified.","section":"Appendix 6.3, Tables 7-8"},{"comment":"SGCE is described as a multi-robot collaborative task, but the experiments never specify the number of robots or the communication model (range, bandwidth, latency) used for map sharing, making the collaborative setting hard to reproduce.","section":"Section 4.1 Benchmark 3, Section 4.5"},{"comment":"The qualitative remarks about Qwen's \"stability\" versus Chat-GLM4's \"action accuracy\", and about the prompt design explaining GPT-4's SGCE result, are not backed by quantitative evidence; please either quantify these claims or remove them.","section":"Section 4.5"},{"comment":"Table 1 spells the platform \"InfinitedWorld\" (missing 'i'); the spelling should be corrected to \"InfiniteWorld\".","section":"Table 1"}],"recommendation":"major_revision","confidential_remarks":"To the editor: this is a broad systems submission, and the main review risk is the breadth-versus-depth tradeoff: each component (generative assets, Real2Sim, annotation, four benchmarks) receives only shallow validation. The all-zero OWSMM results are reported honestly, which I credit, but they currently invalidate the flagship evaluation claim; if the authors cannot demonstrate a positive-control success on OWSMM after repairing the mapping pipeline, the contribution should be re-scoped toward the framework and the two navigation/manipulation benchmarks. I see no circularity or result manipulation; the citations of the authors' own prior work (Surfer, LLPlace) are not load-bearing. Finally, the paper is currently unverifiable because code and data are withheld, so I would treat a full release as a condition for acceptance."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"I read this expecting another wrapper paper, but the infrastructure description is coherent and the authors deserve credit for reporting all-zero results on their flagship benchmark. The genuinely new parts are the SGCE task and the OWSMM hierarchical/horizontal interaction design; everything else is careful assembly of HOLODECK, PGSR, Depth Pro, SAM2Point, and existing assets onto Isaac Sim. That assembly has real value if it ships.\n\nThe soft spots are exactly where your reader puts them, and the stress-test note is on target. OWSMM reports 0.00 success in every condition, and Section 4.5 says the cause is that the benchmark-3 semantic maps are too coarse: object instances missing or positions wrong. That makes the zero an artifact of the upstream mapping, not a measurement of social interaction. The paper's claim that the platform 'comprehensively evaluates' embodied agents is not supported for its most novel benchmark. The same coarse-map problem contaminates the hierarchical condition, because the administrator's knowledge is built from those maps. This is not a manufactured flaw; the manuscript says it itself.\n\nOther weaknesses: no code or data are released (the github link exists, but scenes and assets are 'published upon acceptance'), the Real2Sim improvement is supported only by qualitative figures, and there are no error bars or trial counts anywhere. Navigation and manipulation results mostly show that LLM instruction following with privileged interfaces beats zero-shot VLM, which is not surprising. The SGCE numbers are plausible but thin.\n\nWhat the paper does well: the asset unification story is concrete, the appendix gives a usable post-processing and annotation pipeline, and honest reporting of benchmark failures is rare. The self-citation to Surfer is not load-bearing. Citation formatting is messy, but minor.\n\nMy bottom line: this is a promising infrastructure proposal, not yet a demonstrated benchmark platform. The paper is for simulation and embodied-benchmark people who care about asset interoperability. I would not desk-reject it; I'd send it out with the explicit expectation that the revision ships code and data and either fixes OWSMM or relabels it as a negative result. Without that, it is a system description with a broken evaluation. I would not cite it until the release is real, but for a reading group it is a useful case study in how upstream artifacts can define a benchmark's conclusion.","headline":"A coherent Isaac Sim integration with two genuinely new task designs, but the flagship OWSMM benchmark is invalidated by its own coarse-map artifact and the code/data are not released; worth review only if that changes.","tokens_in":18915,"tokens_out":2611,"would_cite":false,"duration_ms":28728,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper tries to make embodied AI scalable by building InfiniteWorld, a unified Isaac Sim platform for visual-language robot interaction with generative assets and two social benchmarks.","keywords":["embodied AI simulation","visual-language robot interaction","Isaac Sim","generative 3D assets","Real2Sim reconstruction","scene graph exploration","social mobile manipulation","robot benchmarks"],"falsifier":"Replace the coarse exploration maps in open-world social mobile manipulation with ground-truth or object-level scene graphs while keeping every other setting fixed. If the success rate stays at zero, the paper's stated explanation is wrong; if it rises, the mapping pipeline is confirmed as the bottleneck.","tokens_in":17907,"feed_emoji":"🤖","tokens_out":5744,"duration_ms":57802,"temperature":0.7,"pith_summary":"Embodied AI research is currently scattered across many simulators with incompatible assets, so progress toward scaling laws is slower than it could be. This paper tries to fix that with InfiniteWorld, a simulator built on Nvidia Isaac Sim that gathers generative 3D asset creation, a depth-regularized Real2Sim reconstruction pipeline, an automated annotation tool, and converted public datasets into one platform. It also adds four benchmarks, including scene graph collaborative exploration and open-world social mobile manipulation with hierarchical and horizontal interaction. The paper reports that its platform makes navigation and manipulation tractable for LLM-based agents using its interfaces, while VLM zero-shot control fails and the social manipulation benchmark currently scores zero because the semantic maps it inherits are too coarse.","feed_headline":"One simulator unifies assets and adds social robot benchmarks","feed_subtitle":"InfiniteWorld puts generative 3D assets, annotated scenes, and two social tasks on one robot simulation platform.","key_machinery":"The load-bearing object is the unified Isaac Sim–based asset-and-interaction pipeline, with the scene graph collaborative exploration benchmark feeding directly into the open-world social mobile manipulation benchmark. Three sub-machineries carry the asset claim: language-driven scene generation built on HOLODECK with 236 floor and wall texture replacements and 10K base scenes, a Real2Sim pipeline that adds depth and normal regularization losses to PGSR using Depth Pro estimates, and Annot8-3D, a web-based multistage point-cloud annotation framework with AI assistance and optional human-in-the-loop refinement.","core_discovery":"On its own terms, InfiniteWorld is a unified and scalable simulator for visual-language robot interaction built on Nvidia Isaac Sim. The central claim is that one platform can remove the fragmentation slowing embodied-AI research by standardizing asset construction: language-driven scene generation, single-image object reconstruction, controllable articulation generation, a depth-and-normal-regularized Real2Sim pipeline, an automated annotation platform, and conversion of existing scene and object datasets into a common USD format. Alongside the asset machinery, the paper introduces four benchmarks, including scene graph collaborative exploration and open-world social mobile manipulation with two interaction modes: hierarchical, where an administrator agent has more environment knowledge, and horizontal, where equal-status agents exchange knowledge through dialogue. The reported results show LLM-based instruction following succeeding at 90.82% on object loco-navigation and 77.28% on loco-manipulation, near-zero VLM zero-shot performance, and a 0% success rate on social mobile manipulation that the paper attributes to coarse semantic maps inherited from the exploration benchmark.","pith_inferences":["The paper's own diagnosis suggests a near-term extension it does not run: benchmark agents against ground-truth or object-level scene graphs in open-world social mobile manipulation, separating map error from planning and dialogue ability.","If accurate maps unlock nonzero scores, the hierarchical-versus-horizontal contrast could become a useful probe of how much dialogue helps under asymmetric knowledge, going beyond the paper's current all-zero comparison.","The scene generation claim is scalable only if the 236x style substituted scenes remain physically valid and reachable; a scene-level collision or reachability audit could test that validity directly."],"forward_implications":["A single simulator that converts HSSD, HM3D, Replica, ScanNet, 3D-Front, PartNet-Mobility, Objaverse, and ClothesNet assets into one format could let different embodied-AI groups share scenes and objects instead of rebuilding them.","The two new tasks put scene-graph construction and social planning on the evaluation agenda, not just navigation and manipulation.","The large gap between LLM-based action following and VLM zero-shot performance suggests that current vision-language models cannot directly control the robot without explicit navigation and manipulation interfaces.","Because open-world social mobile manipulation reuses scene graph collaborative exploration maps, benchmark results are coupled: exploration quality upper-bounds downstream social manipulation performance.","The reported zero rate on social mobile manipulation is not evidence that social interaction fails; it is evidence that the mapping pipeline must be made reliable before the benchmark can discriminate agent skill."],"supporting_citations":[{"why":"Provides Nvidia Isaac Sim as the foundation platform for physics, rendering, and USD asset integration.","marker":"[47]"},{"why":"HOLODECK is the base of the language-driven 3D scene generation and editing pipeline that produces the 10K scenes.","marker":"[77]"},{"why":"PGSR is the surface reconstruction backbone that the proposed depth and normal regularization losses extend.","marker":"[7]"},{"why":"Depth Pro supplies monocular metric depth priors used in the improved Real2Sim reconstruction pipeline.","marker":"[4]"},{"why":"HSSD provides scene semantics for task generation and is one of the scene assets unified into USD.","marker":"[28]"},{"why":"PartNet-Mobility supplies articulated object assets that are converted into a usable format for manipulation simulation.","marker":"[43]"},{"why":"Co-NavGPT is the LLM multi-agent planning baseline evaluated on scene graph collaborative exploration.","marker":"[80]"},{"why":"Goal-oriented semantic exploration is the baseline method behind the single semantic map comparison in exploration.","marker":"[5]"},{"why":"GPT-4o is used for task instruction generation and serves as the main LLM and VLM evaluated across benchmarks.","marker":"[49]"}],"fun_headline_variants":["Robot sim unifies assets, adds four benchmarks for social tasks","One simulator, four benchmarks for robot visual-language learning","InfiniteWorld: unified robot interaction simulator with social tests","Scaling robot research with unified assets and social benchmarks","New simulator standardizes assets, introduces social interaction benchmarks"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The results of the social manipulation benchmark assume that the semantic maps and scene graphs produced by the exploration benchmark are accurate enough to ground planning, and the paper itself states that these maps were often too coarse, so the zero scores may say more about mapping than about social interaction.","fun_headline_variants_meta":{"raw":{"variants":["Robot sim unifies assets, adds four benchmarks for social tasks","One simulator, four benchmarks for robot visual-language learning","InfiniteWorld: unified robot interaction simulator with social tests","Scaling robot research with unified assets and social benchmarks","New simulator standardizes assets, introduces social interaction benchmarks"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000421,"raw_usage":{"total_tokens":2191,"prompt_tokens":996,"completion_tokens":1195,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":612,"completion_tokens_details":{"reasoning_tokens":1116}},"tokens_in":612,"tokens_out":1195,"duration_ms":12560,"temperature":1.0,"reasoning_tokens":1116,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T20:20:43.736603+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Replace the coarse exploration maps in open-world social mobile manipulation with ground-truth or object-level scene graphs while keeping every other setting fixed. If the success rate stays at zero, the paper's stated explanation is wrong; if it rises, the mapping pipeline is confirmed as the bottleneck.","supporting_citations":[{"cited_title":"Isaac sim 4.0 - robotics simulation and syn- thetic data generation","cited_arxiv_id":null,"evidence_quote":"Provides Nvidia Isaac Sim as the foundation platform for physics, rendering, and USD asset integration."},{"cited_title":"Holodeck: Language guided gen- eration of 3d embodied ai environments","cited_arxiv_id":null,"evidence_quote":"HOLODECK is the base of the language-driven 3D scene generation and editing pipeline that produces the 10K scenes."},{"cited_title":"Chang, and Manolis Savva","cited_arxiv_id":null,"evidence_quote":"HSSD provides scene semantics for task generation and is one of the scene assets unified into USD."},{"cited_title":"Partnet: A large- scale benchmark for fine-grained and hierarchical part-level 3d object understanding","cited_arxiv_id":null,"evidence_quote":"PartNet-Mobility supplies articulated object assets that are converted into a usable format for manipulation simulation."},{"cited_title":"https://openai.com/index/hello-gpt-4o/","cited_arxiv_id":null,"evidence_quote":"GPT-4o is used for task instruction generation and serves as the main LLM and VLM evaluated across benchmarks."}],"review_version":1}