LENS: LLM-guided Environment Simplification for Planning and Control in Clutter
Despite advances in general-purpose robotic manipulation, real-world multi-object clutter remains challenging. LENS, a plug-and-play fix, uses LLMs to automatically generate scene-specific, task-relevant abstractions by merging or pruning entities, improving performance across various manipulation methods.
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[Submitted on 22 Jul 2026]
Title:LENS: LLM-guided Environment Simplification for Planning and Control in Clutter
View a PDF of the paper titled LENS: LLM-guided Environment Simplification for Planning and Control in Clutter, by Aileen Liao and 3 other authors
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Abstract:Despite recent advances in general-purpose robotic manipulation, real-world multi-object clutter remains challenging to handle for today's prevalent approaches. The problem scales in complexity due to more objects and collisions, more unpredictable contact physics, distractors, and task ambiguity. Bridging this gap to real-world deployment requires effective scene abstractions; yet today, producing such abstractions requires extensive task-specific manual engineering, which does not scale. These abstractions are costly to generate and difficult to adjust or fine-tune. We instead propose a plug-and-play fix to automatically generate scene-specific, task-specific, adaptively updating abstractions on top of existing planning and control stacks. LLM-guided Environment Simplification (LENS) produces a de-cluttered abstracted scene representation by merging (e.g., stacked objects) or pruning (e.g., distant objects) scene entities in a closed loop in response to task progress. These dynamic, task-relevant abstractions are versatile and easy to use. In our experiments, we show that LENS improves classical planning, model-based control, and a vision-language-action model, across a diverse set of highly cluttered manipulation scenes. Project website: this https URL.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.19633 [cs.RO]
(or arXiv:2607.19633v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.19633
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Aileen Liao [view email] [v1] Wed, 22 Jul 2026 00:05:00 UTC (12,413 KB)
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