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arxiv: 2606.29358 · v1 · pith:JGLXOXX3new · submitted 2026-06-28 · 💻 cs.RO

LAMP: Long-Horizon Adaptive Manipulation Planning for Multi-Robot Collaboration in Cluttered Space

Shuai Zhou , Yorai Shaoul , Jiaoyang Li This is my paper

Pith reviewed 2026-06-30 07:34 UTC · model grok-4.3

classification 💻 cs.RO
keywords multi-robot manipulationlong-horizon planningcluttered environmentsgenerative modelscontact formationscoupled dynamicsadaptive planninglazy search
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The pith

LAMP combines a learned generative model with systematic and lazy planners to enable long-horizon multi-robot manipulation in cluttered spaces.

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

The paper targets multi-robot manipulation problems that demand joint reasoning over contact formations, coupled robot dynamics, and collision avoidance in spaces that grow intractable with more robots, longer horizons, or denser clutter. Prior approaches either train end-to-end reinforcement-learning policies or reduce the problem to simpler surrogate planning with short-horizon primitives; neither scales to the long-horizon, high-clutter instances considered here. LAMP instead keeps the full coupled search space but uses a learned generative manipulation model to supply action proposals that two new planners can exploit efficiently. A sympathetic reader would care because the method claims to succeed on tasks that existing planners cannot even represent, opening the door to practical multi-robot collaboration in warehouses, homes, or disaster sites.

Core claim

The LAMP framework introduces LAMPA*, a systematic search planner over the full coupled object-robot space, and LAMP-Lazy, a deferred-evaluation planner that supports real-time replanning; both rely on a learned generative manipulation model to generate feasible action proposals, allowing solution of complex long-horizon tasks in highly cluttered environments where prior methods fail.

What carries the argument

The learned generative manipulation model that supplies action proposals, integrated inside the LAMPA* systematic coupled-space search planner and the LAMP-Lazy lazy replanning planner.

If this is right

  • Complex long-horizon multi-robot tasks become solvable in highly cluttered environments where prior methods return no solution.
  • Planning remains tractable as the number of robots and task length increase, provided the generative model supplies adequate proposals.
  • Real-time replanning becomes feasible through deferred evaluation in LAMP-Lazy.
  • The approach maintains the full coupled object-robot search space rather than restricting to surrogate object-only planning.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same proposal-plus-search pattern could be tested on single-robot long-horizon tasks to isolate the benefit of the multi-robot coupling.
  • If the generative model is retrained on data from a different robot morphology, the planners might transfer without retraining the search components.
  • Adding uncertainty over object poses would require only changes to the proposal generator, leaving the planners unchanged.

Load-bearing premise

The learned generative manipulation model produces action proposals that are sufficiently accurate and complete to let the planners discover feasible solutions without missing critical contact formations or dynamics.

What would settle it

A simulated or physical scene containing a known feasible long-horizon sequence of object and robot contacts that the generative model never proposes, causing both LAMPA* and LAMP-Lazy to return no solution.

Figures

Figures reproduced from arXiv: 2606.29358 by Jiaoyang Li, Shuai Zhou, Yorai Shaoul.

Figure 1
Figure 1. Figure 1: Motivating example in a cluttered warehouse envi [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of LAMP framework: Green arrows show the planned path to the goal. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Test cluttered maps with obstacles (black), robots [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Planning time per segment across 100 scenarios. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Path cost across 100 scenarios. Each point represents [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Long-horizon manipulation task: assembling “IROS” [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
read the original abstract

Multi-robot manipulation requires jointly reasoning about contact formations, robot motions under coupled dynamics, and collision avoidance. Systematically searching over this large space is difficult and becomes increasingly intractable as the number of robots grows, the task horizon lengthens, or the scene becomes more cluttered. Existing approaches therefore either learn to solve the problem end-to-end via reinforcement learning or restrict planning to a simpler surrogate problem, such as planning object motions while learning short-horizon contact primitives. However, neither paradigm scales to the problem instances we target: longhorizon multi-robot manipulation in extremely dense environments. In this paper, we propose a Long-horizon Adaptive Manipulation Planning (LAMP) framework with two planners that enable tractable search over the full coupled space by combining a learned generative manipulation model: a LAMPA* planner that systematically searches over the coupled objectrobot space, and LAMP-Lazy: a lazy planner that enables real-time replanning through deferred evaluation. Experiments in challenging simulated environments demonstrate that our approach solves complex long-horizon tasks in highly cluttered environments that prior methods cannot handle.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript proposes the LAMP framework for long-horizon multi-robot manipulation planning in cluttered environments. It combines a learned generative manipulation model with two planners—LAMPA* for systematic search over the coupled object-robot space and LAMP-Lazy for deferred-evaluation replanning—to address the intractability of joint reasoning over contacts, dynamics, and collisions. The central claim is that this enables solution of complex tasks in dense clutter where prior end-to-end RL or surrogate-planning methods fail, as shown by experiments in challenging simulated environments.

Significance. If the experimental claims and model coverage hold, the work would offer a meaningful advance by making full coupled-space search tractable for multi-robot problems without reducing to short-horizon primitives or black-box learning. The hybrid learned-plus-search structure is a reasonable direction for scaling beyond current limits.

major comments (2)
  1. [Abstract] Abstract (and any experimental section): The claim that 'our approach solves complex long-horizon tasks in highly cluttered environments that prior methods cannot handle' is presented without quantitative results, ablation studies, success rates, or comparison metrics. This leaves the central empirical assertion uninspectable and prevents assessment of whether the generative model supplies action proposals complete enough for the planners to succeed.
  2. [Abstract] Abstract: The soundness of LAMPA* and LAMP-Lazy rests on the learned generative manipulation model producing action proposals that cover all critical contact formations and dynamics in the target regimes. No evidence (e.g., recall rates for contact modes, coverage statistics, or failure-case analysis) is supplied to show that the model is sufficiently complete; if any feasible formations are omitted, the subsequent search is incomplete by construction.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on strengthening the empirical support in the abstract. We address each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract (and any experimental section): The claim that 'our approach solves complex long-horizon tasks in highly cluttered environments that prior methods cannot handle' is presented without quantitative results, ablation studies, success rates, or comparison metrics. This leaves the central empirical assertion uninspectable and prevents assessment of whether the generative model supplies action proposals complete enough for the planners to succeed.

    Authors: The manuscript's experimental section provides quantitative results, ablation studies, success rates, and baseline comparisons supporting the claim. We agree the abstract would benefit from including key metrics to make the assertion self-contained. We will revise the abstract to incorporate summary quantitative highlights from the experiments. revision: yes

  2. Referee: [Abstract] Abstract: The soundness of LAMPA* and LAMP-Lazy rests on the learned generative manipulation model producing action proposals that cover all critical contact formations and dynamics in the target regimes. No evidence (e.g., recall rates for contact modes, coverage statistics, or failure-case analysis) is supplied to show that the model is sufficiently complete; if any feasible formations are omitted, the subsequent search is incomplete by construction.

    Authors: The experimental results demonstrate successful task solving in dense clutter requiring diverse contacts where prior methods fail, providing empirical support for sufficient coverage. We acknowledge that explicit coverage analysis would strengthen the soundness argument and will add recall rates for contact modes, coverage statistics, and failure-case analysis in the revision. revision: yes

Circularity Check

0 steps flagged

No circularity detected; claims rest on experimental validation rather than self-referential definitions

full rationale

The provided abstract and context contain no equations, parameter-fitting procedures, self-citations, or uniqueness theorems. The central claims concern empirical performance of LAMPA* and LAMP-Lazy planners that rely on a learned generative model, but the text does not define any quantity in terms of itself or rename fitted outputs as predictions. The derivation chain is therefore self-contained against external benchmarks (simulation experiments), with no load-bearing step reducing to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no equations, algorithms, or experimental sections from which free parameters, axioms, or invented entities can be extracted.

pith-pipeline@v0.9.1-grok · 5721 in / 1175 out tokens · 35114 ms · 2026-06-30T07:34:35.179337+00:00 · methodology

discussion (0)

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