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arxiv: 2605.08330 · v1 · submitted 2026-05-08 · 💻 cs.RO

Recognition: 2 theorem links

· Lean Theorem

Hierarchical Prompting with Dual LLM Modules for Robotic Task and Motion Planning

Karolina \'Zr\'obek , Tessa Pulli , Pawe{\l} Gajewski , Antonio Galiza Cerdeira Gonzalez , Bipin Indurkhya
Authors on Pith no claims yet

Pith reviewed 2026-05-12 01:17 UTC · model grok-4.3

classification 💻 cs.RO
keywords robotic task planningmotion planninglarge language modelshierarchical promptingReActspatial reasoninghuman-robot interactionobject detection
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The pith

Dual LLM modules with hierarchical prompting achieve 86% success in robotic task and motion planning tests

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

The paper presents a framework that splits robotic control into two large language model modules: a high-level ReAct agent that turns natural language commands into sequences of actions such as pick and place, and a dedicated low-level module that performs spatial reasoning on object geometry and scene layout for precise positioning. These modules work with object detection and pose estimation tools to execute the plans on a robot. Evaluation in 24 scenarios that include simple spatial requests, complex instructions, and impossible tasks produced an overall success rate of 86 percent. This separation could allow robots in service settings to follow everyday human directions more reliably than single-model approaches.

Core claim

The hierarchical language-driven framework employs a high-level planning agent using ReAct-style prompts to process natural language commands and generate action sequences, interacting with tools for object perception and manipulation, while a separate low-level spatial reasoning sub-module handles 3D reasoning based on object geometry and scene layout for precise placements, integrated with YOLOX-GDRNet for detection and pose estimation, and evaluated to achieve an 86% task success rate in 24 test scenarios ranging from simple spatial commands to high-level instructions and infeasible requests.

What carries the argument

Hierarchical prompting structure with a primary ReAct-style LLM agent for action sequence generation and a separate spatial sub-module for 3D geometric reasoning

If this is right

  • The approach supports execution of both basic spatial commands and higher-level instructions through language alone.
  • The system can recognize and respond to infeasible requests within the tested range of scenarios.
  • Integration of perception tools with the dual LLM modules produces an end-to-end language-to-action pipeline.
  • Overall task completion reaches 86 percent across commands of varying complexity.

Where Pith is reading between the lines

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

  • Similar hierarchical splits between planning and detailed spatial modules could be tested in other robot skills such as navigation or assembly.
  • Adding execution feedback to adjust spatial outputs in real time might raise success rates beyond the reported level.
  • The dual-module design points to a general strategy for limiting LLM errors on physical reasoning by isolating specific subtasks.

Load-bearing premise

The high-level and low-level LLM modules will reliably interpret natural language and 3D geometry without hallucinations or coordination failures in varied real-world conditions.

What would settle it

Running the system on a physical robot across new environments with novel objects, ambiguous phrasing, or changed lighting and measuring whether the success rate drops well below 86 percent would test whether the dual-module reliability holds.

Figures

Figures reproduced from arXiv: 2605.08330 by Antonio Galiza Cerdeira Gonzalez, Bipin Indurkhya, Karolina \'Zr\'obek, Pawe{\l} Gajewski, Tessa Pulli.

Figure 1
Figure 1. Figure 1: Overview of the Dual LLM Planer: The module reasons how to [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The system architecture integrates a YOLOX-GDRNet vision module [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The adjusted ReAct template prompt provides instructions, a list of available tools with their descriptions and signatures, and specifies the [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Example of a high-level scenario (left). The resulting scene is [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visualization of outputs from vision systems combined with LLM [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
read the original abstract

We present a hierarchical language-driven framework for robotic task and motion planning to improve natural, intuitive human-robot interaction in service and assistance scenarios. The proposed system employs two large language model (LLM) modules: a high-level planning agent and a low-level spatial reasoning sub-module. The primary agent processes natural language commands and generates action sequences using a ReAct-style prompt, interacting with tools for object perception and manipulation (e.g., pick, place, release). For precise spatial placement, such as interpreting "place the mug next to the plate", a separate sub-prompting module handles 3D reasoning based on object geometry and scene layout. The system integrates YOLOX-GDRNet for object detection and pose estimation, along with a motion execution stub. We evaluated the system in 24 test scenarios, ranging from simple spatial commands to high-level instructions and infeasible requests. The system achieved an overall task success rate of 86%.

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

4 major / 1 minor

Summary. The manuscript presents a hierarchical language-driven framework for robotic task and motion planning that uses two LLM modules: a high-level ReAct-style agent for generating action sequences from natural language commands and a low-level spatial reasoning sub-module for precise 3D placements. The system incorporates YOLOX-GDRNet for object detection and pose estimation, along with a motion execution stub, and reports an overall task success rate of 86% across 24 test scenarios.

Significance. If the reported performance holds under more rigorous testing, the dual-module hierarchical prompting could provide a practical way to handle both high-level task planning and low-level spatial reasoning in service robotics, potentially improving intuitiveness over traditional TAMP methods. The work highlights the potential of LLMs in robotics but requires stronger evidence to establish its advantages.

major comments (4)
  1. Abstract: The 86% success rate is presented without any baseline comparisons to classical TAMP solvers, other LLM planners, or ablations of the dual-module design, which is necessary to substantiate the claimed improvement in robotic TAMP.
  2. Abstract: The motion execution is explicitly described as a 'stub,' meaning the success rate measures only high-level planning and perception rather than closed-loop task-and-motion execution, which contradicts the title and central claim of the paper.
  3. Evaluation section: The evaluation on 24 scenarios lacks statistical details, error analysis, failure-mode breakdown, or breakdown by scenario difficulty, making the 86% figure difficult to interpret or generalize.
  4. Abstract: No real-robot validation or discussion of robustness to LLM hallucinations in varied real-world conditions is provided, despite the assumption that the high-level ReAct agent and low-level spatial sub-module will reliably interpret natural language and 3D geometry.
minor comments (1)
  1. Abstract: The range of scenarios from simple spatial commands to infeasible requests is mentioned but not detailed in terms of how infeasible requests are handled or what success means in those cases.

Simulated Author's Rebuttal

4 responses · 1 unresolved

We thank the referee for their thorough and constructive review of our manuscript. We have addressed each major comment point by point below, indicating where revisions will be made to the next version of the paper.

read point-by-point responses

  1. Referee: Abstract: The 86% success rate is presented without any baseline comparisons to classical TAMP solvers, other LLM planners, or ablations of the dual-module design, which is necessary to substantiate the claimed improvement in robotic TAMP.

    Authors: We agree that quantitative baseline comparisons would strengthen the claims of improvement. Our evaluation was designed as a proof-of-concept demonstration of the dual-LLM hierarchical framework across a range of scenarios, including infeasible requests. Full implementation of classical TAMP solvers or extensive ablations would require substantial additional effort beyond the scope of this work. In the revision, we will expand the related work section with a qualitative comparison to existing methods and explicitly note the lack of quantitative baselines as a limitation. revision: partial

  2. Referee: Abstract: The motion execution is explicitly described as a 'stub,' meaning the success rate measures only high-level planning and perception rather than closed-loop task-and-motion execution, which contradicts the title and central claim of the paper.

    Authors: This observation is correct. The motion execution stub means the 86% figure evaluates the planning, spatial reasoning, and perception pipeline with idealized execution. This modular separation is common in TAMP research to focus on planning contributions. We will revise the abstract, introduction, and evaluation sections to explicitly clarify the scope of the reported success rate and note that the framework is designed to integrate with full motion execution modules. revision: yes

  3. Referee: Evaluation section: The evaluation on 24 scenarios lacks statistical details, error analysis, failure-mode breakdown, or breakdown by scenario difficulty, making the 86% figure difficult to interpret or generalize.

    Authors: We will revise the evaluation section to include a breakdown of the 24 scenarios by difficulty category (simple spatial, multi-step, infeasible), success rates per category, and a failure-mode analysis of the unsuccessful cases (e.g., perception errors versus planning issues). Basic statistical details will also be added based on re-examination of the existing results. revision: yes

  4. Referee: Abstract: No real-robot validation or discussion of robustness to LLM hallucinations in varied real-world conditions is provided, despite the assumption that the high-level ReAct agent and low-level spatial sub-module will reliably interpret natural language and 3D geometry.

    Authors: We acknowledge that the current evaluation is limited to the described test scenarios without real-robot experiments. The revision will add a dedicated limitations section discussing assumptions about LLM reliability, potential hallucinations, and robustness issues in real-world settings, along with plans for future real-robot validation. revision: partial

standing simulated objections not resolved
  • Real-robot validation and empirical assessment of robustness to LLM hallucinations under varied real-world conditions

Circularity Check

0 steps flagged

No circularity: empirical system description without derivations or fitted parameters

full rationale

The paper presents a hierarchical LLM framework for robotic TAMP, integrating a ReAct-style high-level planner, a spatial sub-module, YOLOX-GDRNet perception, and a motion execution stub. It reports an 86% success rate across 24 test scenarios as a direct empirical outcome. No equations, parameter fittings, uniqueness theorems, or derivation chains appear in the manuscript. The central claim reduces to measured task completion rates on a fixed scenario set rather than any self-referential reduction of outputs to inputs by construction. This is a standard self-contained empirical systems paper with no load-bearing self-citations or ansatz smuggling.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the framework implicitly relies on standard LLM prompting assumptions and off-the-shelf detection models without new postulates.

pith-pipeline@v0.9.0 · 5482 in / 1090 out tokens · 52531 ms · 2026-05-12T01:17:18.584472+00:00 · methodology

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Reference graph

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