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ALRM: Agentic LLM for Robotic Manipulation

Vitor Gaboardi dos Santos, Ibrahim Khadraoui, Ibrahim Farhat, Hamza Yous, Samy Teffahi, Hakim Hacid

arXiv:2601.19510v2cs.ROcs.CL

TL;DR

Prior LLM-based robotic control approaches often lack modular closed-loop execution, and manipulation benchmarks do not systematically test multistep reasoning and linguistic variation. ALRM combines a ReAct-style loop with code-generation and tool-calling modes and evaluates them on a diverse manipulation benchmark. Claude-4.1-Opus led closed-source results, while Falcon-H1-7B performed strongly under CaP.

  • Problem

    Prior LLM-based robotic control approaches often lack modular mechanisms for planning, reflecting on outcomes, and revising actions, while benchmarks under-test multistep reasoning and linguistic variation.

    Source: §I · p. 1

  • Method

    ALRM pairs ReAct-style planning and execution with code-generation (CaP) and tool-calling (TaP) modes, evaluated on a high-level, linguistically diverse manipulation benchmark.

    Source: §I · pp. 1–2

  • Results

    Claude-4.1-Opus led closed-source results; closed-source models improved with TaP at higher latency, while Falcon-H1-7B matched DeepSeek-V3.1 under CaP with much lower latency.

    Source: §I · p. 2, §VII · p. 8

  • Takeaways & Limitations

    Small-scale open-source models perform well at generating executable robot code but struggle with tool-based execution for high-level reasoning tasks.

    Source: §VII · p. 8

    The evaluation is limited to three environments and mainly pick-and-place tasks in simulation, without integration of real robots and perception components.

    Source: §VII · p. 8

Abstract

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Large Language Models (LLMs) have recently empowered agentic frameworks to exhibit advanced reasoning and planning capabilities. However, their integration in robotic control pipelines remains limited in two aspects: (1) prior \ac{llm}-based approaches often lack modular, agentic execution mechanisms, limiting their ability to plan, reflect on outcomes, and revise actions in a closed-loop manner; and (2) existing benchmarks for manipulation tasks focus on low-level control and do not systematically evaluate multistep reasoning and linguistic variation. In this paper, we propose Agentic LLM for Robot Manipulation (ALRM), an LLM-driven agentic framework for robotic manipulation. ALRM integrates policy generation with agentic execution through a ReAct-style reasoning loop, supporting two complementary modes: Code-asPolicy (CaP) for direct executable control code generation, and Tool-as-Policy (TaP) for iterative planning and tool-based action execution. To enable systematic evaluation, we also introduce a novel simulation benchmark comprising 56 tasks across multiple environments, capturing linguistically diverse instructions. Experiments with ten LLMs demonstrate that ALRM provides a scalable, interpretable, and modular approach for bridging natural language reasoning with reliable robotic execution. Results reveal Claude-4.1-Opus as the top closed-source model and Falcon-H1-7B as the top open-source model under CaP.

I. INTRODUCTION

Prior LLM-based robotic control and manipulation benchmarks leave gaps in closed-loop reflection, multistep reasoning, and linguistic variation. ALRM combines agentic execution with two policy modes and introduces a benchmark for evaluating diverse high-level tasks.

I. INTRODUCTION

  • Prior LLM-robotics approaches often lack static-planning alternatives, interpretability, and closed-loop reflection, while existing benchmarks underrepresent multistep reasoning and linguistic variation.Benchmark instructions are not systematically varied in wording, syntax, or reasoning level.Source: §I · p. 1
  • ALRM combines LLM-based action generation with a ReAct-style loop for iterative reasoning, execution monitoring, and plan revision through Code-as-Policy and Tool-as-Policy modes.CaP runs executable control code directly; TaP executes actions through LLM tool calling.Source: §I · p. 1
  • The proposed benchmark targets high-level multistep tasks spanning lexical variation to complex reasoning, while the contribution summary specifies 56 linguistically diverse manipulation instructions.The paper also reports developing a Gazebo simulation environment integrated with ROS and MoveIt for testing and deployment.Source: §I · pp. 1–2
  • Claude-4.1-Opus led closed-source results at 93.5% TaP and 92.6% CaP average success, while Falcon-H1-7B matched DeepSeek-V3.1 at 84.3% CaP with less than half the latency.Experiments evaluated ten LLMs across task success rate and execution latency in both modes.Source: §I · p. 2

II. RELATED WORK

The related work distinguishes explicit robot control from LLM-generated code or action sequences, then positions ALRM as a modular agentic framework alongside a benchmark designed for more complex, linguistically diverse evaluation.

II. RELATED WORK

  • Explicit control maps perception and language to low-level motor commands, while implicit control generates code or API actions executed through existing control infrastructure.The cited explicit-policy examples include π0 [15], OpenVLA [16], and Gemini Robotics [17]; implicit approaches include Code-as-Policy [6] and SayCan [7].Source: §II · p. 2
  • ALRM combines code generation and tool-based execution with agent coordination, reflection on outcomes, and closed-loop task revision.CaP directly generates executable control code, whereas TaP uses LLM tool calling.Source: §II · p. 2
  • Prior benchmarks differ in scope and language diversity: RLBench offers many tasks but limited linguistic variation, while RoboCasa includes composite tasks with straightforward definitions and task-relevant objects.ACRV is a physical shelf-picking benchmark with limited task definitions and language diversity, and no simulation environment.Source: §II · p. 3
  • ALRM's benchmark focuses on high-level tasks requiring at least two sequential pick-and-place actions, with four difficulty levels and distractor objects that require selecting relevant items.Tasks range from synonym substitutions to reasoning such as selecting fruits by calorie content.Source: §II · p. 3

III. LLM-BASED ROBOTIC AGENT ARCHITECTURE FOR TASK PLANNING AND EXECUTION

The architecture separates high-level planning, subtask execution, and robot/API operations. Execution results return to the planner as observations, enabling further actions until task completion or a step limit.

III. LLM-BASED ROBOTIC AGENT ARCHITECTURE FOR TASK PLANNING AND EXECUTION

  • The architecture comprises a task planner agent, a task executor agent, and an API server.The planner produces high-level subtasks, the executor turns them into actions, and the server runs those actions in simulation or on a real robot.Source: §III · p. 3
  • After each execution, the executor summarizes results as observations for the planner, which uses that feedback to generate subsequent actions until the task is fulfilled or the maximum number of steps is reached.The planner incrementally creates subtasks based on environment and robot interactions.Source: §III · p. 3

A. Task Planner Agent

The planner decomposes user requests into one-step subtasks using environment feedback, while the executor translates each subtask into actions available through either TaP or CaP.

A. Task Planner Agent

  • The ReAct-based planner dynamically decomposes high-level requests into subtasks and adapts them using observations from the executor.Its prompt templates cover object positions and names as well as atomic manipulation actions; guidelines limit each step to one action and one relevant object.Source: A. Task Planner Agent · pp. 3–4

B. Task Executor Agent

  • The executor converts a natural-language subtask into actions using eight predefined controls, perception operations, and pose-retrieval actions, available through TaP and CaP.The action set includes pick, place, move_to_home_pos, move_to, get_objects, get_reference_names, compute_grasp, and get_pose.Source: B. Task Executor Agent · p. 4

1) Tool-as-Policy (TaP):

ALRM supports interactive tool-based execution and single-run code execution, backed by a robotics API that exposes high-level manipulation and perception functions.

1) Tool-as-Policy (TaP):

  • TaP executes one tool call at a time, feeding responses back to the LLM so it can reason over intermediate results and correct mistakes.This flexibility requires more LLM calls and reliable tool calling.Source: 1) Tool-as-Policy (TaP) · p. 4

2) Code-as-Policy (CaP):

  • CaP generates Python code for a subtask and runs it once, reducing LLM calls and supporting models without tool-calling capabilities.An error in generated code can fail the entire subtask, so CaP is more brittle and relies more on accurate planner descriptions.Source: 2) Code-as-Policy (CaP) · p. 4

C. API Server for Robotic Control

  • The REST API connects the wx250s arm to high-level pick-and-place, motion, and perception functions through robotics and simulation modules.This interface lets the LLM focus on high-level actions rather than low-level ROS commands and provides a reproducible experiment interface.Source: C. API Server for Robotic Control · p. 4

IV. BENCHMARK

The benchmark tests multistep manipulation across simulated environments using canonical tasks and paraphrases that vary linguistic form and reasoning demands.

IV. BENCHMARK

  • The benchmark combines three simulated environments, three canonical tasks per environment, and five paraphrases per task, totaling 54 instructions.Ground-truth code and tool-call sequences were validated in Gazebo, and the benchmark is publicly available.Source: §IV · pp. 4–5

A. Environments

  • The three environments cover kitchen utensils, boxes, and fruits, with object sets that require reasoning about properties and positions.The fruits environment also includes a bowl and trash.Source: A. Environments · p. 5

B. Tasks

  • Tasks require manipulating at least two objects and may involve execution order, spatial references, or object properties such as weight, size, and category.Each environment has three canonical tasks expressed as direct imperatives.Source: B. Tasks · p. 5

IV. BENCHMARK

  • Each canonical task receives five paraphrases to test language variation beyond the original instruction.The benchmark is designed around controlled environments, structured tasks, and linguistically diverse instructions.Source: §IV · p. 4, B. Tasks · p. 5

C. Paraphrase Categories

The benchmark categorizes paraphrases by linguistic and reasoning challenge, then frames evaluation around model selection, metrics, and experimental design.

C. Paraphrase Categories

  • Four paraphrase categories, inspired by prior paraphrasing literature, [22], distinguish lexical, syntactical, semantic, and high-level reasoning changes.Each paraphrase receives one label for its core change, and high-level reasoning has two examples per task because it represents more challenging scenarios.Source: C. Paraphrase Categories · p. 5

V. EXPERIMENTAL SETUP

  • The experimental setup covers LLM selection, evaluation metrics, and the overall evaluation design.Source: §V · p. 5

A. Large Language Models

The evaluation compares ten LLMs in CaP and TaP using success rate and latency, with success judged against task requirements rather than exact action-sequence matching.

A. Large Language Models

  • Ten LLMs are evaluated in both CaP and TaP, including large models and models up to 8B parameters to examine performance and cost trade-offs.The selected models include GPT-5, Gemini-2.5-Pro, Claude-4.1-Opus, DeepSeek-V3.1, Falcon-H1-7B, Qwen3-8B, Llama-3.1-8B, DeepSeek-R1-7B, Granite-3.3-8B, and Mistral-7B.Source: A. Large Language Models · pp. 5–6

B. Evaluation Metrics

  • Success rate measures whether generated actions solve the task, using an LLM judge because multiple action sequences can be valid.The judge receives the task, ground-truth sequence, and generated sequence, then assigns a score from 0 to 2 based on task completion.Source: B. Evaluation Metrics · p. 6
  • Three judges distinct from the evaluated models score each task by majority vote, averaging their scores if all three disagree.Their temperature is fixed at 0.0 for consistency and reproducibility.Source: B. Evaluation Metrics · p. 6
  • The evaluation reports average success rate across all 56 instructions, category-level success rates, and average task latency.The categories are Canonical, Lexical, Syntactical, Semantics, and High-Level Reasoning.Source: B. Evaluation Metrics · p. 6

C. Evaluation Design

The evaluation compares executor modes, models, and paraphrase categories using a lightweight simulation setup whose generated actions were also validated in Gazebo. Results show a success–latency trade-off between CaP and TaP, with different patterns for large- and small-scale models.

C. Evaluation Design

  • Evaluation varies executor mode, LLM, and paraphrase category, while the table reports success-score counts, per-category percentages, and latency in seconds.The lightweight environment accepts correctly formatted movement parameters and placeholder poses; ground-truth code and tool calls were validated in Gazebo with an Interbotix wx250s arm.Source: C. Evaluation Design · p. 6, Table III · p. 7

VI. RESULTS

  • Table III summarizes performance across modes, LLMs, and paraphrase categories, grouping models into large-scale and small-scale sets for comparison.The large-scale group has four models; the small-scale group contains six models, each up to 8B parameters.Source: §VI · p. 6

A. Operation Mode Comparison

  • Falcon-H1-7B achieved 84.3% success with CaP at 24.89 seconds, while Qwen3-8B and Llama-3.1-8B averaged 64.8% and 68.5%, respectively.Some small models generated executable control code with CaP, but small-scale models generally struggled with TaP.Source: A. Operation Mode Comparison · pp. 6–7
  • Claude-4.1-Opus reached 93.5% average success with TaP, 0.9% above CaP, while latency rose from 33.44s to 82.60s; GPT-5 favored CaP, 90.7% versus 85.2%.Most large-scale models had higher success with TaP but increased latency; GPT-5 was the exception.Source: A. Operation Mode Comparison · p. 6, Table III · p. 7
  • Small-scale models often struggled with TaP; Qwen3-8B reached 53.7% success but averaged 392.08 seconds, and consecutive nested calls often led to partial or failed pick-and-place tasks.The authors attribute Qwen3-8B's latency to its thinking output.Source: A. Operation Mode Comparison · p. 7

B. LLMs Comparison

Large-scale models generally lead on performance, while Falcon-H1-7B is a strong small-scale CaP model. Small-scale results vary, and some models lack tool-calling support or struggle to generate useful actions.

B. LLMs Comparison

  • Among large-scale LLMs, Claude-4.1-Opus had the best performance across TaP and CaP in success rate and latency, while GPT-5's 145.59s CaP latency limits practical usability.Gemini-2.5-Pro and DeepSeek-V3.1 performed similarly, and no large-scale model scored zero.Source: B. LLMs Comparison · p. 7
  • Falcon-H1-7B achieved 84.3% CaP success, 11.2% above Gemini-2.5-Pro and equal to DeepSeek-V3.1, with 24.89s processing time.The paper notes that Falcon-H1-7B has far fewer parameters than those larger models.Source: B. LLMs Comparison · p. 7
  • DeepSeek-R1-7B does not support tool calling, limiting its use to execution modes that do not require that capability.The code-based executor can use models without function or tool calling.Source: B. LLMs Comparison · p. 7
  • Small-model CaP results ranged from 53.7% success for Granite-3.3-8B to 8.3% for Mistral-7B, with planning errors often producing meaningless actions.Qwen3-8B and Llama-3.1-8B achieved 68.5% and 21.3% for Llama-3.1-8B and DeepSeek-R1-7B, respectively.Source: B. LLMs Comparison · p. 7

C. Task Categories

Large-scale models perform consistently across task categories, whereas small-scale CaP models are strongest on canonical and syntactical tasks and weakest on high-level reasoning. The paper recommends model choices by deployment context and identifies benchmark and real-world evaluation limits.

C. Task Categories

  • For large-scale models, lexical tasks had the highest average success rate at 92.3%, while canonical, high-level reasoning, semantics, and syntactical tasks averaged 88.8%, 84.3%, 84.7%, and 84.0%, respectively.The authors interpret the pattern as larger models struggling more with task difficulty than natural-language variation.Source: C. Task Categories · pp. 7–8
  • Small-scale LLMs in CaP averaged 54.6% on canonical tasks, 53.7% on syntactical tasks, 51% on lexical and semantic tasks, and 45.8% on high-level reasoning.Falcon-H1-7B and Qwen3-8B each reached 88.9% on syntactical tasks.Source: C. Task Categories · p. 8

VII. CONCLUSION

  • The authors recommend Claude-4.1-Opus for API use and Falcon-H1-7B for local execution, while calling for small-scale models with stronger nested tool-calling abilities.Their conclusion reports strong small-scale CaP results but difficulty with tool-based execution for high-level reasoning tasks.Source: §VII · p. 8
  • The benchmark covers only three environments and mainly pick-and-place tasks, and the experiments validate simulation rather than integration with real robots and perception components.The authors also note that results may change with different planning and execution prompts.Source: §VII · p. 8
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