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REVIEW 4 major objections 3 minor 1 cited by

MORE: Mobile Manipulation Rearrangement Through Grounded Language Reasoning

T0 review · 4 major / 3 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read MORE is the first LLM-based planner to solve a large share of BEHAVIOR-1K rearrangement tasks, reporting 48.1% success across 81 tasks.

desk verdict A solid planning-level contribution with honest disclosure of its shortcuts; the 48.1% result should be read as measuring planning under idealized perception and magic actions, not as solving BEHAVIOR-1K end-to-end. read the letter →

arxiv 2505.03035 v1 pith:4ZYHB6IO submitted 2025-05-05 cs.RO cs.AI

Mohammad Mohammadi , Daniel Honerkamp , Martin Büchner , Matteo Cassinelli , Tim Welschehold , Fabien Despinoy , Igor Gilitschenski , Abhinav Valada This is my paper · ORCID
classification cs.ROcs.AI
keywords mobilemanipulationrearrangementtasksscenegraphsLLMplanningBEHAVIOR-1KinstancedifferentiationgraphfilteringVoronoi
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes MORE, a planner that uses a large language model to solve long-horizon mobile manipulation rearrangement tasks in large, partially unexplored indoor and outdoor environments. It claims that representing the world as a hierarchical scene graph, filtering that graph down to task-relevant object and region instances, and giving each object instance a distinct identifier lets an LLM plan reliably where prior LLM- and VLM-based planners hallucinate or get lost. On 81 BEHAVIOR-1K tasks, MORE reports a 48.1% success rate and 80.1% relative task progression, the first substantial share of that benchmark solved. The paper also contributes fully specified natural-language task descriptions and accelerated "magic action" subpolicies that make large-scale evaluation feasible, alongside real-world demonstrations in a three-room apartment.

What carries the argument

The load-bearing object is the task-informed subgraph $\xi_{task}$ produced by an LLM-based active filtering function $f: \xi \to \xi_{task}$. The full scene graph, spanning root, regions, and objects with alphabetical instance IDs and inferred attributes, is rendered as structured text; the LLM is prompted to delete objects irrelevant to the task or to finding task-relevant objects, yielding a bounded planning problem that is then given to a second LLM call selecting among object-centric subpolicies (navigate, open, close, grasp, place_inside, place_ontop, explore, done). Supporting machinery includes a Voronoi graph sparsified by removing degree-two nodes with close neighbors in $O(|V|)$ time, region separation at doors using Gaussian kernels matched to door bounding boxes, and assignment of objects to navigable connected components so unreachable regions are excluded from planning.

What would settle it

Run MORE on the same 81 BEHAVIOR-1K tasks with physical Fetch arm control enabled and with a real open-vocabulary segmentation model instead of ground-truth semantics; if the success rate falls far below 48.1% or manipulation steps fail often, the reported result is planning-only. A smaller check is to run 20 trials of each of the three real-world tasks with the N2M2 policy and AR-marker perception, counting completed goal conditions; if completion drops below the reported 3/3, low-level execution is the bottleneck.

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Extended reading notes

Core claim

The central discovery claimed is that task-relevant subgraph filtering, rather than more powerful reasoning alone, is what unlocks LLM-based planning at scale: by compressing a scene with hundreds of objects into a small subgraph $\xi_{task}$ before invoking the planner, the LLM's action choices stay grounded. MORE couples this with instance differentiation (alphabetical IDs so the model can track multiple daffodils, boxes, or bottles), a sparsified Voronoi navigation graph, door-aware region separation, and MPC-style replanning after each subpolicy. The result is the first method reported to complete a significant share of BEHAVIOR-1K: 48.1% success rate, 50.6% total task completion, 70.1% task progression, and 80.1% relative task progression across 81 tasks, outperforming MoMa-LLM and BUMBLE with and without the additional filtering.

Load-bearing premise

The load-bearing premise is that replacing real arm control with "magic actions" and using ground-truth semantic perception still tells us how the method would behave with real perception and manipulation; if those shortcuts do not faithfully represent real-world conditions, the 48.1% success rate supports only the high-level planning component.

Editorial extensions

If this is right

  • MORE reports the first substantial success on BEHAVIOR-1K rearrangement tasks, with 48.1% success and 50.6% total task completion, showing that LLM planners can handle hundreds of objects when given a filtered scene.
  • Scene graph filtering alone improves a baseline: BUMBLE's success rate rises from 11.3% to 15.7% when filtering is added, indicating the filtering scheme transfers as a module.
  • Instance differentiation closes part of the gap: MoMa-LLM with spatial relations goes from 19.7% to 36.1%, and MORE reaches 48.1%, so tracking distinct object instances matters for multi-object tasks.
  • The gap between 70.1% task progression and 80.1% relative task progression suggests most failures come from knowing when to stop searching or satisfying all constraints, not from failing to find the relevant objects.
  • Real-world demonstrations on set-the-table, clean-up, and fetch-and-store tasks show the same planner can drive an HSR robot in a multi-room apartment, though low-level execution errors such as a late gripper release can still occur.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: the active filtering step is a task-relevance bottleneck that could likely be swapped into other LLM- or VLM-based planners; the paper's own BUMBLE-plus-filtering result supports this.
  • Inference: because arm movements are replaced by magic actions and semantics come from ground truth, the benchmark numbers should be read as planning ability; end-to-end performance in the real world will depend on perception and manipulation components the paper only partially exercises.
  • Inference: the TP/rTP gap points to an explicit stopping or "done" reasoning problem, and future work could add ambiguity resolution by asking the LLM whether unobserved areas might still contain required object instances.
  • Inference: a testable extension would run the same filtering and planning pipeline with an open-vocabulary perceptual scene graph instead of ground-truth semantics, which would reveal how much of the 48.1% success rate survives realistic perception.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 3 minor

Summary. The paper proposes MORE, a language-model-based planner for long-horizon mobile manipulation rearrangement. It constructs a hierarchical 3D scene graph from RGB-D observations, filters the graph to a task-relevant subgraph using an LLM prompt, and uses a second LLM to select object-centric subpolicies in a model-predictive-control loop. The authors add instance differentiation, a Voronoi-graph sparsification scheme, and region separation at doors to support indoor-outdoor scenes. They evaluate MORE on 81 hand-picked tasks from the BEHAVIOR-1K benchmark, reporting a 48.1% success rate and 80.1% relative task progression, outperforming MoMa-LLM and BUMBLE baselines, along with three qualitative real-world tasks. The simulation evaluation relies on ground-truth semantics and 'magic actions' that bypass physical manipulation, as acknowledged in Sections III-A and S.2.

Significance. If the claims are taken at face value, MORE demonstrates that LLM-based planning with explicit scene-graph memory and active filtering can make substantial progress on long-horizon rearrangement tasks at scale. The paper's strengths include a reproducible benchmark subset (public task descriptions and accelerated subpolicies), a clear system description, and a strong improvement over the MoMa-LLM predecessor. However, the reported results are obtained under strongly idealized perception and execution assumptions, so the significance is limited to high-level planning rather than embodied mobile manipulation. The absence of multiple seeds and ablations further tempers the strength of the comparisons.

major comments (4)
  1. [Section III-A, Section S.2] The evaluation uses ground-truth semantics and 'magic actions' that disable arm movements and bypass physical simulation. Consequently, the reported 48.1% SR and 80.1% rTP measure high-level action selection on a symbolic state-update abstraction, not mobile manipulation in BEHAVIOR-1K as an embodied benchmark. The abstract claims MORE is 'the first approach to successfully solve a significant share of the benchmark' and the title refers to 'Mobile Manipulation Rearrangement'; these claims overstate what is demonstrated. The authors should explicitly qualify all such claims as planning under idealized perception and execution, or add results from actual physical simulation.
  2. [Section IV-A, Table I] The results are single-run point estimates with no error bars, multiple seeds, or discussion of LLM sampling temperature. Because GPT-4o is stochastic, the 48.1% SR and the relative ordering of methods need statistical support. The metric definition for Success Rate is also self-contradictory: it states SR requires 'done() called correctly' and then says 'this does not require the model call done() correctly after doing so'. Please fix the metric definitions and report variance.
  3. [Section III-A, Table S.1] The 81-task subset is hand-selected from the 1,000 BEHAVIOR-1K tasks, with scenes (restaurant diner, school gym) and tasks with failed initialization excluded. The paper should analyze selection bias: how representative are these 81 tasks of the full benchmark, and how many of the 163 tasks involving articulated objects were removed due to initialization failure? Without such analysis, the claim of solving 'a significant share of the benchmark' is only established for a curated subset, and the 'first approach' claim is not meaningful relative to the original benchmark.
  4. [Section IV-B] The paper does not ablate MORE's components. The comparison BUMBLE + filtering shows that the filtering alone helps BUMBLE, but no experiment removes filtering, instance differentiation, or Voronoi sparsification from MORE itself. Without ablations such as 'MORE without filtering' or 'MORE without instance IDs', it is unclear which components drive the improvement over MoMa-LLM + spatial relations. Please add at least one such condition to support the claimed contributions.
minor comments (3)
  1. [Section IV-A, Section S.5] There are typos: 'BUBMLE' should be 'BUMBLE' in the baseline description, and 'halucinations' appears in Section S.5 and should be 'hallucinations'.
  2. [Section S.2] In the low-level action space, 'turn-left by up to 35 rad' and 'turn-right by up to 35 rad' are presumably meant to be small angles such as 0.35 rad; 35 radians is physically nonsensical.
  3. [Figure 2 caption] The caption text 'BtOccupancy map GV' is garbled; it should read something like 'Occupancy map B_t, Voronoi graph G_V' to clearly label the components.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: MORE's benchmark results are external evaluations, and its hand-set constants and self-citations do not define the measured outcomes.

full rationale

The paper's central claim—that MORE solves a substantial subset of BEHAVIOR-1K rearrangement tasks—is grounded in empirical evaluation against an external benchmark, not in a derivation from its own inputs. No fitted parameter is defined in terms of the reported success metrics; the hand-set constants (λ=1.3, the Voronoi sparsification threshold, and the door-kernel threshold) are implementation choices rather than quantities learned from or equivalent to the benchmark outcomes. The authors' self-citations to MoMa-LLM establish architectural lineage and a baseline for comparison, but they are not load-bearing in a circular sense: the paper does not invoke a uniqueness theorem from prior work, and the 'first approach' claim is supported by running MORE and the baselines on the same task set, not by the cited papers themselves. The task descriptions are generated from the BDDL goal conditions, which is a form of benchmark curation rather than a prediction derived from the model; the planner still must select a valid action sequence, and the measured goal-condition completion is not identical to the prompt text by construction. Similarly, the magic-action and ground-truth-semantics assumptions are explicit simplifications that weaken ecological validity—the 48.1% success rate measures high-level action selection on an idealized interaction abstraction rather than embodied mobile manipulation—but this is a scope and validation concern, not circularity. No step in the paper reduces, by definition or by self-citation, to its own inputs.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The method rests on four main assumptions: perfect perception, magic action execution, a hand-selected benchmark subset, and LLM reliability. Free parameters are confined to scene graph construction, not to fitted task outcomes.

free parameters (3)
  • Object distance exponent lambda (Eq. 1) = 1.3
    Chosen by hand to prioritize Voronoi nodes closer to objects; no ablation or justification is provided, and it affects object-to-region assignment and therefore the scene graph given to the LLM.
  • Voronoi sparsification threshold c (Algorithm 1)
    Threshold for merging degree-two Voronoi nodes; the numeric value is not reported even though it is required to run the algorithm.
  • Door Gaussian kernel integral threshold
    Threshold that determines whether an edge crosses a door and separates regions; the value is not reported in the supplement.
assumptions (4)
  • domain assumption Accurate semantic perception is assumed; ground-truth semantics are used in simulation and a pre-annotated map in real world.
    Stated in Sec. III-A and S.2. This removes perception error from the evaluation, so results do not transfer to real perception pipelines.
  • domain assumption Magic actions faithfully emulate manipulation outcomes.
    Stated in Sec. III-A: subpolicies remove physical simulation of arm movements. Success rates depend on these non-physical completions.
  • domain assumption The selected 81-task subset and authored natural-language descriptions are a valid proxy for BEHAVIOR-1K.
    Tasks are hand-picked, some scenes are skipped, and descriptions are written by the authors from BDDL goals; this affects the generalization claim.
  • domain assumption GPT-4o provides a reliable filtering and planning function f.
    The core method relies on LLM outputs with no guarantees or calibration, so results could shift with model version or prompt changes.

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Cite this review

Pith. "Pith review of MORE: Mobile Manipulation Rearrangement Through Grounded Language Reasoning." pith.science (2026). https://pith.science/paper/4ZYHB6IO

@misc{pith2026250503035,
  author       = {Pith},
  title        = {Pith review of: MORE: Mobile Manipulation Rearrangement Through Grounded Language Reasoning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4ZYHB6IO}},
  note         = {Machine review of arXiv:2505.03035}
}
read the original abstract

Autonomous long-horizon mobile manipulation encompasses a multitude of challenges, including scene dynamics, unexplored areas, and error recovery. Recent works have leveraged foundation models for scene-level robotic reasoning and planning. However, the performance of these methods degrades when dealing with a large number of objects and large-scale environments. To address these limitations, we propose MORE, a novel approach for enhancing the capabilities of language models to solve zero-shot mobile manipulation planning for rearrangement tasks. MORE leverages scene graphs to represent environments, incorporates instance differentiation, and introduces an active filtering scheme that extracts task-relevant subgraphs of object and region instances. These steps yield a bounded planning problem, effectively mitigating hallucinations and improving reliability. Additionally, we introduce several enhancements that enable planning across both indoor and outdoor environments. We evaluate MORE on 81 diverse rearrangement tasks from the BEHAVIOR-1K benchmark, where it becomes the first approach to successfully solve a significant share of the benchmark, outperforming recent foundation model-based approaches. Furthermore, we demonstrate the capabilities of our approach in several complex real-world tasks, mimicking everyday activities. We make the code publicly available at https://more-model.cs.uni-freiburg.de.

Figures

Figures reproduced from arXiv: 2505.03035 by the authors.

Figure 1
Figure 1. We present MORE, an efficient model for the task of rearrangement [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of MORE. Starting in an unexplored environment, we continuously construct a hierarchical scene graph of the environment based on [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Language-based scene graph filtering: We employ a filtering prompt [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (1 more)
Figure 5
Figure 5. Figure 5: Real-world experiments. From top left to bottom right: overview of [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

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