FISRF: Flexible Instructional Scaffolding Reflection Framework for Robotics Task Planning with Large Language Model

Qingmiao Liang; Jiatao Zhang; Runxin Zou; Yufan Song; Wei Song; Yuhua Zheng

cs.ROOct 26, 2026

FISRF: Flexible Instructional Scaffolding Reflection Framework for Robotics Task Planning with Large Language Model

Authors: Qingmiao Liang, Jiatao Zhang, Runxin Zou, Yufan Song, Wei Song, Yuhua Zheng

Organizations: Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China · Zhejiang University, Hangzhou, China

Abstract

Large Language Models (LLMs) are widely adopted for reflective error correction in task planning; however, existing approaches primarily rely on autonomous exploration, rendering them prone to accumulating erroneous experiences during early interactions with new environments. To address this limitation, we propose the Flexible Instructional Scaffolding Reflection Framework (FISRF), an agent--mentor architecture that estimates the robot's Zone of Proximal Development (ZPD) in task planning and adaptively selects reflection strategies and mentor guidance, thereby facilitating the accumulation of effective error-correction experience. Experiments in ALFWorld and on real-world robotic platforms demonstrate that FISRF reduces the average number of error-correction rounds by 37% and redundant reasoning by 25.1%, while improving the overall task success rate by 8%. Project webpage : https://fisrfplanning.github.io/

Figures & tables

Fig. 1: Compared with existing approaches, FISRF adaptively determines whether to solicit mentor guidance and selects appropriate reflection strategies according to the difficulty of correction and the magnitude of improvement, in each trial, thereby effectively preserving and leveraging valuable experience for subsequent correction.

Fig. 2: The framework of FISRF. The planning process is executed by the Actor, the evaluation process is executed by Evaluator, the reflection process is executed by the Self-Reflection, the difficulty of correction and magnitude of improvement are assessed, according to the assessment results, determine whether to seek mentor guidance and implement appropriate reflection strategies. By integrating insights from autonomous exploration with mentor guidance, our framework inform subsequent plan. The resulting reflections and planning trajectories are stored in a memory module for long-term retrieval.

Fig. 3: The detailed illustration of FISRF. The difficulty of correction and magnitude of improvement are first assessed, determining reflection strategies and whether to seek mentor guidance. After that, the detailed self-reflection process consists of the valuable experience summary process, the failure lesson summary process, Seek mentor guidance and the Reflection Summary process. Final output of the self-reflection process is a improved new plan for the next attempt.

Fig. 4: In an ALFWorld example, FISRF corrects a failed trajectory by summarizing experiences in the first episode, subsequently learning from failures and seeking mentor guidance in the second episode.

Method	Success Rate (%) $\uparrow$
Method	Put	Clean	Heat	Cool	Examine	SR@1	SR@2	Overall
Model: Gemini 2.5
Plan-Only	82.9	71.0	82.6	90.5	66.7	73.9	76.1	79.1
Reason-Only	90.2	80.6	79.2	95.7	55.6	76.1	82.1	83.6
Reason-Reflect	97.6	80.6	78.3	90.5	66.7	76.1	81.3	85.1
Reason-Flex Reflect	100.0	87.1	60.9	90.5	88.9	78.4	85.8	87.3

TABLE I: Success rates (%) of FISRF and baseline methods on ALFWorld across different task categories. Results are reported under Gemini 2.5 and GPT-4o model

Methods	Efficiency				Flexibility
Methods	$\mathrm{Cost}_{\mathrm{ref}}$ @4 $\downarrow$	$\mathrm{Rate}_{\mathrm{rev}}(\%)$ @4 $\uparrow$	$\mathrm{Round}_{\mathrm{rev}}$ @2 $\downarrow$	$\mathrm{Round}_{\mathrm{rev}}$ @4 $\downarrow$	R $\uparrow$	$\text{CV}(\%)$ $\uparrow$
${Model:Gemini}$ ${2.5}$
Reason-Reflect	913.7	64.3	3.26	4.79	207	23.5
Reason-Flex Reflect	558.3	69.6	2.30	3.15	237	21.5
FISRF(Ours)	370.5	87.5	1.45	1.71	253	36.7
${Model:GPT}$ ${4o}$

TABLE II: Flexibility and Efficiency of reflection methods. Our FISRF demonstrates significantly higher flexibility and efficiency.

Fig. 5: Food collection task of a failed task successfully refined using our FISRF.

Fig. 6: Result of success rate and round revision with different episodes. Our FISRF outperforms in all episodes, converges more quickly and has a significant better efficiency.

Method	SR	R	CV	$\mathbf{Round_{rev}}$	$\mathbf{Cost_{ref}}$	$\mathbf{Rate_{rev}}$
	(%)	( $\uparrow$ )	( $\uparrow$ )	( $\downarrow$ )	( $\downarrow$ )	( $\uparrow$ )
Full	94.8	253	36.7	1.7	370.5	87.5
w/o Exp	87.3	198	24.5	3.6	487.3	68.7
w/o Lesson	88.8	208	22.1	2.4	472.5	75.3
w/o Mentor	82.9	272	27.6	4.2	550.7	63.5

TABLE III: Ablation study of FISRF components in AlfWorld tasks. All designed modules contribute to the performance.

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Large Reasoning Language Models (LRLMs) enable multi-step reasoning for robotic task planning, but continued reasoning can overwrite valid intermediate plans or leave constraint violations unresolved, reducing planning reliability and wasting inference-time computation. We develop an inference-time monitor that exposes and verifies intermediate plans without disrupting the original decoding trajectory. Building on this monitor, we propose SafeInferCom, a formal verifier-guided framework that preserves valid intermediate plans and directs error correction during generation. Experiments across multiple LRLMs and planning domains reveal reasoning-response inconsistency and limited self-correction under one-shot inference. SafeInferCom improves planning success and accelerates error correction relative to one-shot inference. When combined with iterative refinement, it further improves success while reducing token usage compared with refinement alone. We additionally evaluate SafeInferCom in VirtualHome and provide a real-world robotic-arm demonstration.

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