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arxiv: 2509.14787 · v2 · pith:GDL7RQWJnew · submitted 2025-09-18 · 💻 cs.RO

COMPASS: Confined-space Manipulation Planning with Active Sensing Strategy

Qixuan Li , Chen Le , Dongyue Huang , Jincheng Yu , Xinlei Chen This is my paper

Pith reviewed 2026-05-21 22:02 UTC · model grok-4.3

classification 💻 cs.RO
keywords confined spacemanipulation planningactive sensingexplorationsampling-based planningmulti-objective optimizationrobot benchmarkcluttered environments
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The pith

COMPASS improves robot manipulation success in confined spaces by combining active sensing with planning.

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

The paper presents COMPASS as a framework for robots to explore and manipulate objects in tight, cluttered areas where visibility is limited. It starts with a scan close to the robot to map nearby obstacles and reduce collision chances. Then it uses a utility function that scores potential viewpoints not just for new information but also for how well they set up future manipulation actions. A constrained optimization step creates safe poses for grasping while staying clear of obstacles. The method is evaluated on a custom benchmark with four difficulty levels, showing a 24.25% increase in successful manipulations over standard exploration approaches in simulations, with real-world tests confirming its practical use.

Core claim

By incorporating manipulation awareness into the exploration planner through multi-objective viewpoint selection and constrained pose optimization, the COMPASS framework enables more effective and safer manipulation in partially observable confined environments compared to information-gain-only methods.

What carries the argument

The manipulation-aware sampling-based planner that employs a multi-objective utility function for viewpoint selection and a constrained manipulation optimization strategy to respect obstacle constraints.

If this is right

  • Manipulation success rate increases by 24.25% in simulations compared to other exploration methods.
  • The framework reduces collision risks using near-field awareness scans to build local collision maps.
  • It generates manipulation poses that respect obstacle constraints in confined spaces.
  • Real-world experiments validate the capability for active sensing and manipulation.
  • The four-level benchmark allows systematic evaluation of performance under increasing difficulties.

Where Pith is reading between the lines

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

  • Integrating sensing and action planning in this way may generalize to other robotics tasks with high uncertainty, such as search and rescue in rubble.
  • Future work could adapt the multi-objective weights dynamically based on task priorities.
  • Applying similar active sensing strategies to non-manipulation tasks like navigation in clutter could yield comparable gains.
  • Validation on a wider range of robot hardware and sensor types would test the framework's robustness.

Load-bearing premise

The four-level benchmark scenarios and the specific multi-objective utility weights adequately capture the real difficulties of confined-space manipulation and that observed gains are attributable to the manipulation-aware components rather than implementation details or baseline weaknesses.

What would settle it

An experiment showing that a standard information-gain exploration method achieves comparable or higher manipulation success rates in the same confined scenarios, or that the near-field scan fails to prevent collisions in real tests.

Figures

Figures reproduced from arXiv: 2509.14787 by Chen Le, Dongyue Huang, Jincheng Yu, Qixuan Li, Xinlei Chen.

Figure 1
Figure 1. Figure 1: Overview of COMPASS, a framework for active perception and manipulation in confined spaces. (Top) The three-stage pipeline solves a real-world task by sequentially performing: (I) a Near￾Field Awareness Scan for safety, (II) MUE-RRT to find the target, and (III) Constrained Grasp optimization. (Bottom) Our principled simulation benchmark systematically evaluates performance across four levels of increasing… view at source ↗
Figure 2
Figure 2. Figure 2: Firstly, a map node incrementally builds an octomap [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of system framework of COMPASS. Depth images from the wrist-camera are used to incrementally build an OctoMap. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The runtime logic of a successful exploration and manipu [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The four challenging scenarios of our proposed benchmark, [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 4
Figure 4. Figure 4: As shown, our approach consistently achieves the [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Target volume exploration performance over time across the [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Snapshots of real-world experiments. build a map of the confined space and search for the target ( [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
read the original abstract

Manipulation in confined and cluttered environments remains a significant challenge due to partial observability and complex configuration spaces. Effective manipulation in such environments requires an intelligent exploration strategy to safely understand the scene and search the target. In this paper, we propose COMPASS, a multi-stage exploration and manipulation framework featuring a manipulation-aware sampling-based planner. First, we reduce collision risks with a near-field awareness scan to build a local collision map. Additionally, we employ a multi-objective utility function to find viewpoints that are both informative and conducive to subsequent manipulation. Moreover, we perform a constrained manipulation optimization strategy to generate manipulation poses that respect obstacle constraints. To systematically evaluate method's performance under these difficulties, we propose a benchmark of confined-space exploration and manipulation containing four level challenging scenarios. Compared to exploration methods designed for other robots and only considering information gain, our framework increases manipulation success rate by 24.25% in simulations. Real-world experiments demonstrate our method's capability for active sensing and manipulation in confined environments.

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

1 major / 1 minor

Summary. The manuscript presents COMPASS, a multi-stage framework for manipulation planning in confined and cluttered spaces. It combines a near-field awareness scan to construct a local collision map, a multi-objective utility function that balances information gain with manipulation feasibility for viewpoint selection, and a constrained optimization step to generate valid manipulation poses. The authors introduce a four-level benchmark of increasing difficulty for confined-space tasks and report that their approach achieves a 24.25% higher manipulation success rate than prior exploration methods (designed for other robots and focused solely on information gain) in simulation, with supporting real-world experiments.

Significance. If the performance gains can be shown to be statistically robust and attributable to the manipulation-aware components rather than baseline implementation differences, the work would provide a useful integration of active sensing and constrained manipulation planning. The structured four-level benchmark is a constructive contribution that could facilitate more comparable evaluations in this domain.

major comments (1)
  1. [Evaluation section] Evaluation section (results reporting the 24.25% success-rate gain): The central empirical claim lacks essential supporting details, including the number of independent trials, measures of statistical significance or variance, exact baseline implementations (e.g., whether constrained pose optimization or near-field collision maps were added to the comparison methods), and any post-hoc scenario selection criteria. Without these, it is not possible to determine whether the reported delta arises from the proposed multi-objective utility and constrained optimization or from unadapted baselines.
minor comments (1)
  1. [Abstract] Abstract: 'four level challenging scenarios' should be written as 'four-level challenging scenarios' for standard hyphenation and readability.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the single major comment on the evaluation section below and commit to revisions that strengthen the empirical reporting without altering the core claims or experimental design.

read point-by-point responses

  1. Referee: [Evaluation section] Evaluation section (results reporting the 24.25% success-rate gain): The central empirical claim lacks essential supporting details, including the number of independent trials, measures of statistical significance or variance, exact baseline implementations (e.g., whether constrained pose optimization or near-field collision maps were added to the comparison methods), and any post-hoc scenario selection criteria. Without these, it is not possible to determine whether the reported delta arises from the proposed multi-objective utility and constrained optimization or from unadapted baselines.

    Authors: We agree that the manuscript would benefit from greater transparency in the evaluation section. In the revised version we will explicitly state the number of independent trials run per benchmark level, report variance (standard deviation) across trials, and include statistical significance testing (e.g., paired t-tests) for the observed 24.25% improvement. We will also clarify that the baselines were the original information-gain-only implementations from the cited works, without the addition of our near-field collision mapping or constrained pose optimization; this choice was deliberate to isolate the benefit of the full manipulation-aware pipeline. Finally, we will confirm that every scenario in the four-level benchmark was evaluated in full, with no post-hoc filtering or selection of results. These additions will make the attribution of performance gains clearer while preserving the original experimental protocol. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper describes an empirical robotics framework (COMPASS) with components for near-field scanning, multi-objective viewpoint selection, and constrained pose optimization, evaluated on a four-level benchmark via simulation success rates and real-world trials. No equations, derivations, or first-principles results are presented that reduce the reported 24.25% success-rate gain to quantities fitted from the same data or defined in terms of the output itself. Comparisons are framed against external methods, and no load-bearing self-citations or uniqueness theorems are invoked to force the central claims. The evaluation remains independent of the method's internal definitions.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central performance claim rests on the effectiveness of an unspecified multi-objective utility function and on the representativeness of the new benchmark scenarios; both involve design choices whose tuning details are not visible in the abstract.

free parameters (1)
  • weights in multi-objective utility function
    Balances information gain against manipulation feasibility; values not reported in abstract and likely chosen or fitted to achieve reported gains.
axioms (1)
  • domain assumption Sampling-based planners can be extended to respect manipulation constraints while remaining computationally tractable in confined spaces
    Invoked by the description of the manipulation-aware planner and constrained optimization step.

pith-pipeline@v0.9.0 · 5707 in / 1004 out tokens · 38306 ms · 2026-05-21T22:02:28.636621+00:00 · methodology

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