Interleaved LLM and Motion Planning for Generalized Multi-Object Collection in Large Scene Graphs

Fumin Zhang; Mengxue Hou; Ruochu Yang; Yu Zhou

REVIEW 4 major objections 5 minor 31 references

Interleaved LLM and Motion Planning for Generalized Multi-Object Collection in Large Scene Graphs

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Interleaving LLM planning with motion-cost feedback cuts household robot mission costs by 30%.

desk verdict Real interleaving idea, but the cost estimator is an unnormalized sum and the 30% claim doesn't hold as stated. read the letter →

arxiv 2507.15782 v1 pith:FTOZDNGX submitted 2025-07-21 cs.RO

Ruochu Yang , Yu Zhou , Fumin Zhang , Mengxue Hou This is my paper

classification cs.RO

keywords interleavedplanningLLMtaskmotionscenegraphscostestimationmulti-objectcollectionmobilemanipulationlong-horizonmissions

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

The paper tries to show that a household robot can fulfill long, multi-command collection missions by interleaving an LLM planner with a motion planner instead of letting the LLM plan in semantic space and then executing blindly. Inter-LLM uses a multimodal action cost similarity function to estimate costs of unseen navigation and manipulation actions from previously measured costs, so the LLM can prune expensive actions before the robot attempts them. In a photo-realistic home simulation with a mobile manipulator, the authors report a 30% improvement in overall mission performance over the SayPlan and MoMa-LLM baselines, with near-optimal plans generated on the fly. If correct, this makes LLM-based household planning responsive to real physical difficulty—clutter, collisions, hard-to-reach objects—rather than relying on language commonsense alone.

What carries the argument

The central object is the multimodal action cost similarity function $F^{\mathrm{ms}}$—a two-part estimator that assigns costs to unseen high-level actions by comparing them with already executed actions. On the navigation side, the estimator computes the symmetric path-overlap percentage $P_o(p_i,p_j)$ between the presumed A* path of a candidate navigate action and every executed path with a known cost; the unknown navigation cost is the overlap-weighted sum of those known costs (Eq. 12). On the manipulation side, the estimator prompts the LLM with the object's location, category, and usage together with known costs labeled hard/medium/easy, asks it to infer a textual cost for the unseen pickup/place action (Eq. 17), and decodes the label to a numerical value (Eq. 18). The function's estimates are summed over each candidate plan (Eq. 20) to rank the M LLM-generated plans, and the executed costs are fed back into the known set by averaging (Eq. 9), which is what lets the planner improve over the course of a mission.

What would settle it

Run Inter-LLM on a held-out set of start-and-destination furniture pairs whose presumed paths have low overlap with all previously executed paths, and compare the plan ranked cheapest by Eq. (12) with the plan that has the lowest actually measured navigation cost; if the predicted plan is not the measured cheapest in a majority of trials, the similarity-based cost estimator is selecting the wrong plans. For manipulation, compare the LLM-inferred easy/medium/hard labels with empirically measured pickup and place costs on a held-out object set; large disagreement would break the semantic estimator.

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Extended reading notes

Core claim

The central claim is that an LLM-based task planner and a sampling-based motion planner can be interleaved so that the LLM estimates candidate task-plan costs before execution, using a multimodal action cost similarity function that reflects the history and looks into the future. For navigation, an unknown cost is computed as a path-overlap-weighted sum of previously measured navigation costs (Eq. 12), where overlap between the presumed A* path and each executed path is a symmetric percentage (Eq. 11). For manipulation, the LLM acts as a semantic similarity function: given the object's location, category, and usage plus known manipulation costs encoded as easy/medium/hard, it infers an unknown cost label that is decoded to a number (Eqs. 17–19). The total estimated cost of each of M candidate plans is then compared (Eq. 20), the cheapest feasible plan is executed, and the measured cost of each executed action is averaged into the known-cost set (Eq. 9). The paper reports that this loop yields a 30% improvement in overall mission performance over the SayPlan and MoMa-LLM baselines across ten three-command missions involving up to 24 actions.

Load-bearing premise

The load-bearing premise is that the multimodal similarity function estimates unknown action costs accurately enough to order candidate plans: navigation cost is predicted from path overlap with previously measured paths, and manipulation cost from object location, category, and usage, so if either proxy is systematically wrong the planner can choose a plan that looks cheap but is not.

Editorial extensions

If this is right

A long-horizon mission can be optimized before execution: the cost-similarity estimate prunes high-cost navigate, pickup, and place actions from LLM-generated plan candidates, so the robot avoids attempting expensive actions in the real world.
The robot's plans improve as the mission progresses: every executed action adds a measured cost to the known set, and the averaged updates make later estimates better grounded, which is why the reported gain grows as the mission length increases.
Reactive replanning after failures is not needed for unseen difficulties: a hard action is avoided in advance when its estimated cost is high, rather than being tried and retried.
The planner's quality is governed by the accuracy of the two cost estimators; if they rank candidate plans correctly, the algorithm can approach optimal plans without exhaustively searching the action-state space.

Reading between the lines

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

A direct extension the paper leaves untested is applying the same interleaving structure to other long-horizon task-and-motion domains—logistics, inspection, search-and-rescue—by swapping in domain-appropriate cost models for navigation and manipulation; the interleaving loop itself is not specific to household scenes.
The paper's temperature study (Table II) suggests the manipulation-cost estimator is sensitive to the LLM sampling temperature, so a calibrated confidence score or an ensemble of decodes would likely make the easy/medium/hard inference more reliable than the single-sample prompt used in the main experiments.
The 30% figure comes from one procedural environment; a sharper evaluation would vary the scene graph and object sets across trials to test whether the path-overlap proxy remains informative when the map layout changes.
The path-overlap estimator assumes untried paths cost like similar tried paths; if a never-traversed shortcut is much cheaper, the estimator will miss it, and a learned cost model trained on the same measured costs could correct for that gap.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Main take: this paper has a real idea worth discussing, but the central cost estimator is mathematically off, so the headline 30% claim doesn't hold as stated.

What's new: they formulate a generalized multi-object collection mission across multiple commands in a scene graph, and instead of one-shot LLM planning, they interleave: LLM proposes M candidate plans, a low-level motion planner executes and records costs, and a multimodal similarity function estimates costs of unseen navigation and manipulation actions to prune future candidates. That feedback loop is not in SayPlan or MoMa-LLM, and it's a sensible direction. The feasibility checker is also a practical plus.

Where it gets soft: Eq. (12) is not a weighted average, it's a raw sum. The estimate for a navigation action is the sum over all known paths of known cost times overlap percentage. That means a candidate path that resembles many historical paths gets a much larger estimated cost than the same path with fewer historical neighbors, even though true cost is unchanged. The estimate isn't on the same scale as known costs, and it can't meaningfully rank candidates as the mission grows. Eq. (20) has a related problem: multiplying manipulation cost by N_valid/N_man discounts a plan when many actions are unknown, which systematically favors plans with unknown (zero-cost) actions. These aren't minor typos; they're the mechanism that selects plans.

The evaluation is also weaker than the abstract implies. Table III shows Inter-LLM fulfills 4/9 objects while MoMa-LLM fulfills 7/9, so "maximizing mission success rates" is not what the data shows. The 30% improvement is on a hand-weighted composite metric, with no error bars, and appears to be from a single scene (or average over ten missions not clearly reported). The baselines are treated inconsistently: doors are obstacles for Inter-LLM but openable for MoMa-LLM, which makes the comparison favorable to the authors' method.

The paper is not a waste of time—the interleaving idea is worth developing, and the authors correctly identify a gap in LLM planners ignoring low-level costs. Roboticists working on LLM-planning pipelines will find a useful, if flawed, example of explicit cost feedback. But the cost-similarity function needs rethinking, and the claims need to be narrowed.

I'd send it to review—a serious referee can help fix the estimator and the eval—but I wouldn't cite the current version as a reliable method. Good candidate for a reading group discussion on why cost heuristics need scale awareness.

Recommendation: major revision or reject based on the estimator; not desk-reject-worthy.

Referee Report

4 major / 5 minor

Summary. The paper addresses a generalized multi-object collection problem in large scene graphs, where a mobile manipulator must fulfill multiple abstract human commands by navigating among rooms/furniture and manipulating open-set objects. The authors propose Inter-LLM, an interleaved planning algorithm in which a high-level LLM planner generates task plan candidates and a low-level sampling-based motion planner provides empirical action costs. A 'multimodal action cost similarity function' estimates unknown navigation and manipulation costs so the LLM can prune expensive branches. The paper claims a 30% improvement in overall mission performance over the SayPlan and MoMa-LLM baselines while producing 'near-optimal plans on the fly.'

Significance. The core idea of closing the loop between LLM semantic planning and low-level motion planning with cost feedback is timely and potentially useful for long-horizon household tasks. The problem formulation is clear, the hierarchical decomposition is sensible, and the feasibility checker addresses a real issue with LLM hallucinations. However, the paper's central mechanism—the multimodal similarity cost estimator—is mathematically flawed as written, and the empirical evidence does not support the headline claims. The method, if corrected, might have value, but the current manuscript does not establish its validity.

major comments (4)

[Section IV-C, Eq. (12)] The estimated navigation cost in Eq. (12) is an unnormalized sum, \hat c(a_nav_k) = Σ_i c_naved_i · P_o(p_pre_k, p_naved_i). Because P_o is a similarity percentage between 0 and 200, the estimate grows linearly with the number of stored known paths N_naved. For the same candidate action, presenting one known path versus ten known paths changes the estimate by roughly 10x even though the true cost is unchanged. This makes \hat c scale-inconsistent over the mission and invalidates the use of the estimate to compare plan candidates across commands, undermining the claim of 'near-optimal on the fly.' The estimator should be normalized (e.g., by Σ_i P_o) or otherwise made independent of the size of C_naved.
[Section IV-C, Eq. (20)] The multiplicative discount N_man_valid/N_man in Eq. (20) systematically favors plans with many unknown manipulation actions. Since unknown costs are decoded to 0 in Eq. (18), a plan with many unknowns receives a low estimated total, even though the actual costs are unknown and could be high. For example, a plan with two known medium-cost actions and eight unknown actions gets its known-cost sum multiplied by 0.2, making it appear much cheaper than a plan with the same two known actions and no unknowns. This is backwards for cost-aware planning and would tend to select risky, untested actions.
[Section V-C, Table III and Abstract] The results in Table III contradict the abstract's claim of 'maximizing mission success rates.' The object fulfillment rate row shows Inter-LLM fulfilling 4 of 9 objects, whereas MoMa-LLM fulfills 7 of 9. Moreover, using the metric in Eqs. (22)-(23) with the hyperparameters in Table I, the single mission presented yields moverall ≈ 1536 for SayPlan, ≈ 1553 for Inter-LLM, and ≈ 2084 for MoMa-LLM; thus Inter-LLM is worse than SayPlan on the stated overall metric. The claimed 30% improvement over 'the baselines' is therefore not reproducible from the reported data, and no error bars or multiple-scene statistics are provided to support the claim.
[Section IV-C and Section V-B] The multimodal similarity function's accuracy is never validated against held-out ground-truth costs. Section V-B reports only qualitative path-overlap examples and a temperature sweep for one semantic inference scenario, but does not show whether the estimated costs correctly rank candidate plans or generalize across the mission. The arbitrary thresholds (5/15) in Eq. (13) and decoded values (5/10/20) in Eq. (18) are additional free parameters that could materially affect plan selection.

minor comments (5)

[Section IV-C, Eq. (11)] There is a typo in the text: 'two paths' is written as 'twp paths.'
[Table I and Eq. (22)] The notation is inconsistent: Eq. (22) uses γ_nav while Table I lists the collision-count normalizing factor as γ_n. Please use consistent subscripts.
[Section V-B, Table II] The 'Semantic Similarity Accuracy' metric is not defined; it is unclear whether it measures agreement with human labels, with true execution costs, or something else.
[Section V-C] The text states that ten missions were evaluated, but Table III appears to present a single mission with nine objects. Please clarify how the table relates to the ten-mission evaluation and report aggregate statistics with variance.
[Algorithm 1, lines 5-9] The feasibility-checker loop has no termination guarantee if the LLM repeatedly generates invalid plans; a bounded retry or fallback mechanism should be described.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the cost-similarity loop estimates unknowns from prior measurements, and the core comparison is external to the fitted values.

full rationale

The derivation chain is not circular. The interleaved planner estimates unknown navigation costs via Eq. (12) from previously measured navigation costs and unknown manipulation costs via Eqs. (17)-(19) from known examples; this is a k-nearest-neighbor-style estimation rule, not a quantity defined in terms of the conclusion it is used to reach. The evaluation is against external simulation metrics (Table III, Fig. 4) on missions not used to fit the cost estimators, and the manually chosen thresholds (Eqs. 13, 18) and normalization weights (Table I) are fixed hyperparameters rather than fitted-to-target predictions. The self-citations ([13], [19]) are motivational and do not supply a load-bearing theorem or forbid alternative designs. The reviewer-level concern about Eq. (12)'s unnormalized sum growing with N_naved and Eq. (20)'s N_man_valid/N_man factor favoring unknown actions is a substantive correctness/robustness problem, but it is not circularity: the estimated cost is not identical by construction to the ground-truth cost or to the evaluation metric, and no predicted quantity is defined as a fitted parameter.

Assumptions & free parameters 8 free parameters · 6 assumptions · 0 invented entities

All of the method's predictions stem from similarity heuristics and hand-chosen weights rather than from a derived optimum. The central claim inherits these choices: the 30% improvement is measured against a metric whose weights (gamma_nav=10, gamma_man=100, gamma_obj=100) and whose cost estimators are chosen by the authors. No parameter-free prediction is made that was not used to set constants.

free parameters (8)

gamma_nav = 10
Normalizing factor for navigation collision counts in Eq. 7 and in the overall metric Eq. 22 (Table I). Hand-chosen to scale collision count against time and distance.
gamma_man = 100
Normalizing factor for manipulation failure rate in Eq. 8 and overall metric Eq. 23 (Table I). Hand-chosen.
gamma_obj = 100
Normalizing factor for object fulfillment rate in overall metric Eq. 23 (Table I). Hand-chosen; drives the tradeoff that lets Inter-LLM win despite fulfilling fewer objects.
LLM temperature sigma = 0.8
Temperature for LLM semantic similarity inference in Table II; 0.8 gives the best reported accuracy (73%), so it is tuned on the evaluation data.
number of task plan candidates M = 3
LLM generates M candidate plans per command (Algorithm 1 line 2, Table I). Small fixed value; no sensitivity analysis is provided.
path overlap distance threshold epsilon_d = not reported
Hyperparameter in Eq. 11 that decides when two path points count as overlapping. No value or sensitivity analysis is given.
cost quantization thresholds and decode values = hard: >15, medium: 5-15, easy: <5; decode: 20/10/5/0
Hand-chosen mapping from measured manipulation costs to textual levels and back (Eq. 13 and 18). The thresholds 5 and 15 and decoded values 20, 10, 5, 0 are arbitrary.
low-level sample count N_l = not reported (example uses 5)
Number of sampled low-level states used to compute empirical action cost in Eq. 6; no value is specified in Table I.

assumptions (6)

domain assumption Adding navigation and manipulation costs into a single additive objective (Eq. 2, Eq. 7, Eq. 8) captures the true mission cost.
The planner and the evaluation metric both assume costs are separable and additive, and that no other costs (e.g., replanning overhead) matter; this is an unproven modeling choice.
domain assumption Uniformly sampling free nodes in the occupancy grid gives a representative set of low-level states, so the average of trial costs approximates the true action cost (Eq. 6).
No convergence or bias analysis is provided; the example uses only 5 samples around a sofa.
ad hoc to paper Path overlap percentage P_o (Eq. 11) is a valid proxy for similarity of navigation costs.
This geometric overlap heuristic with hand-chosen epsilon_d is introduced solely to enable cost transfer; no empirical validation against ground-truth navigation costs is reported beyond a self-consistency plot.
ad hoc to paper An LLM can infer manipulation costs from semantic attributes (location, category, usage) and a few known cost examples (Eq. 13-17).
This is the core of the manipulation cost estimator; Table II reports accuracy at different temperatures but no ground-truth generalization test with held-out object/furniture pairs.
domain assumption The PDDL-style feasibility checker (Section IV-A) is sufficient to guarantee the validity of LLM-generated task plans.
It checks only preconditions, effects, and scene-graph membership; it does not check geometric reachability or dynamic collisions.
domain assumption The environment transition function V in Eq. 4 is deterministic and known.
The problem statement says "high uncertainties" but the formulation assumes deterministic state transitions; stochastic outcomes are only handled reactively after execution failures.

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Cite this review

Pith. "Pith review of Interleaved LLM and Motion Planning for Generalized Multi-Object Collection in Large Scene Graphs." pith.science (2026). https://pith.science/paper/FTOZDNGX

@misc{pith2026250715782,
  author       = {Pith},
  title        = {Pith review of: Interleaved LLM and Motion Planning for Generalized Multi-Object Collection in Large Scene Graphs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FTOZDNGX}},
  note         = {Machine review of arXiv:2507.15782}
}

read the original abstract

Household robots have been a longstanding research topic, but they still lack human-like intelligence, particularly in manipulating open-set objects and navigating large environments efficiently and accurately. To push this boundary, we consider a generalized multi-object collection problem in large scene graphs, where the robot needs to pick up and place multiple objects across multiple locations in a long mission of multiple human commands. This problem is extremely challenging since it requires long-horizon planning in a vast action-state space under high uncertainties. To this end, we propose a novel interleaved LLM and motion planning algorithm Inter-LLM. By designing a multimodal action cost similarity function, our algorithm can both reflect the history and look into the future to optimize plans, striking a good balance of quality and efficiency. Simulation experiments demonstrate that compared with latest works, our algorithm improves the overall mission performance by 30% in terms of fulfilling human commands, maximizing mission success rates, and minimizing mission costs.

Figures

Figures reproduced from arXiv: 2507.15782 by the authors.

**Figure 2.** Predicates, preconditions, and effects of high-level actions [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Preliminary results of path similarity. The more overlapped the two paths are, the higher the path similarity percentage is. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Results of algorithm comparison by overall cost metric. We [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

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M. Deitke, E. VanderBilt, A. Herrasti, L. Weihs, J. Salvador, K. Ehsani, W. Han, E. Kolve, A. Farhadi, A. Kembhavi, and R. Mottaghi, “ProcTHOR: Large-Scale Embodied AI Using Procedural Generation,” in NeurIPS, 2022, outstanding Paper Award

2022

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Navigating to objects in the real world,

T. Gervet, S. Chintala, D. Batra, J. Malik, and D. S. Chaplot, “Navigating to objects in the real world,” Science Robotics , vol. 8, no. 79, p. eadf6991, 2023

2023

[2] [2]

Tidybot: Personalized robot assistance with large language models,

J. Wu, R. Antonova, A. Kan, M. Lepert, A. Zeng, S. Song, J. Bohg, S. Rusinkiewicz, and T. Funkhouser, “Tidybot: Personalized robot assistance with large language models,” Autonomous Robots , vol. 47, no. 8, pp. 1087–1102, 2023

work page 2023

[3] [3]

Integrated task and motion planning,

C. R. Garrett, R. Chitnis, R. Holladay, B. Kim, T. Silver, L. P. Kael- bling, and T. Lozano-P ´erez, “Integrated task and motion planning,” Annual review of control, robotics, and autonomous systems , vol. 4, pp. 265–293, 2021

work page 2021

[4] [4]

Sequential manipulation planning on scene graph,

Z. Jiao, Y . Niu, Z. Zhang, S.-C. Zhu, Y . Zhu, and H. Liu, “Sequential manipulation planning on scene graph,” in 2022 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 8203–8210

work page 2022

[5] [5]

Hierarchical planning for long-horizon manipulation with geometric and symbolic scene graphs,

Y . Zhu, J. Tremblay, S. Birchfield, and Y . Zhu, “Hierarchical planning for long-horizon manipulation with geometric and symbolic scene graphs,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 6541–6548

work page 2021

[6] [6]

Task and motion planning in hierarchical 3d scene graphs,

A. Ray, C. Bradley, L. Carlone, and N. Roy, “Task and motion planning in hierarchical 3d scene graphs,” arXiv preprint arXiv:2403.08094 , 2024

arXiv 2024

[7] [7]

Sayplan: Grounding large language models using 3d scene graphs for scalable robot task planning,

K. Rana, J. Haviland, S. Garg, J. Abou-Chakra, I. Reid, and N. Suen- derhauf, “Sayplan: Grounding large language models using 3d scene graphs for scalable robot task planning,” in 7th Annual Conference on Robot Learning , 2023

work page 2023

[8] [8]

Saynav: Grounding large language models for dynamic planning to navigation in new environments,

A. Rajvanshi, K. Sikka, X. Lin, B. Lee, H.-P. Chiu, and A. Velasquez, “Saynav: Grounding large language models for dynamic planning to navigation in new environments,” in Proceedings of the International Conference on Automated Planning and Scheduling , vol. 34, 2024, pp. 464–474

work page 2024