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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

arXiv:2610.35686v2cs.RO

TL;DR

Early interactions with new environments can leave robots without effective experience for unfamiliar problems, while the robot’s planning ZPD lacks a quantitative operational formulation. FISRF adapts reflection and mentor guidance using task-performance factors, and reports gains across ALFWorld tasks compared with baselines.

Abstract

from arXiv · show

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/

I. INTRODUCTION

FISRF addresses the difficulty of accumulating effective experience during early interactions with new environments by adapting reflection and mentor guidance to a robot’s task-planning ZPD. It combines an agent–mentor architecture with task-performance-based guidance and is evaluated on ALFWorld.

I. INTRODUCTION

  • Prior LLM planning work includes reflective feedback, tree-search reflection, environment-specific rule libraries, and complexity-adaptive reflection.The introduction cites RETROFORMER [4], Reflexion [5], Expel [10], AutoManual [11], FCRF [12], and Thinkless [13].Source: §I · p. 1
  • Existing reflection methods often rely on autonomous exploration, while early errors in new environments can accumulate into ineffective attempts and reduced efficiency.The paper identifies the lack of an operational formulation of a robot’s ZPD in task planning as a key challenge.Source: §I · p. 1, in new environments while maintaining… · p. 1

in new environments while maintaining a certain degree of autonomy?

  • Instructional scaffolding frames the ZPD as the gap between what a learner can do independently and what appropriate guidance makes possible.The paper proposes progressively withdrawing support and consolidating assisted experience into autonomous competence.Source: in new environments while maintaining… · p. 1
  • FISRF combines an Actor, Evaluator, and Self-Reflection components with a retrieval-augmented Mentor that supplies state-conditioned guidance.The Evaluator models correction difficulty and magnitude of improvement to determine ZPD entry and guide reflection and mentor use.Source: in new environments while maintaining… · pp. 1–2
  • ALFWorld experiments organize tasks into six categories and report superior FISRF performance across all task types.The paper also reports validation on real-world robotic tasks.Source: in new environments while maintaining… · p. 2

II. RELATED WORKS

Prior work develops LLM task planning through sequential representations, reasoning-action interleaving, executable programs, and grounding in affordances. Reflection-based agents extend planning with feedback, experience accumulation, structured environments, and tree search.

A. LLMs for Task Planning

  • LLM task-planning approaches represent observations and histories sequentially, interleave reasoning with actions, encode plans as programs, or ground language in affordances.Examples include LID [16], ReAct [6], CodeAsPolicy [17], ProgPrompt [1], AdaPlanner [18], SayCan [19], and LLMPlanner [20], [21].Source: A. LLMs for Task Planning · p. 2

B. LLM Agents for Self-Reflection

  • LLM reflection methods use feedback to refine plans, accumulate experience across tasks, structure environments, or combine reflection with tree search.FCRF [12] adapts reflection intensity according to task complexity.Source: B. LLM Agents for Self-Reflection · p. 2

A. Planning Framework

The paper frames task planning as finding an action sequence that reaches a goal and reflection as an iterative feedback loop. FISRF’s Actor generates plans from the goal, observations, and prior reflection summaries, with trajectories and reflections retained for subsequent trials.

A. Planning Framework

  • A task is represented by its goal, state space, observation space, action set, and state-transition dynamics.Planning seeks an action sequence that guides the system from its initial state to a state satisfying the goal.Source: A. Planning Framework · p. 2

B. Self-Reflection Process in LLM-based Planning

  • Reflection iteratively maps an environment-evaluated trajectory to linguistic feedback stored in memory and used to update later planning.The loop continues until environmental feedback indicates successful task completion.Source: B. Self-Reflection Process in… · p. 2

IV. METHODOLOGY

  • FISRF organizes planning, flexible self-reflection, short-long memory management, and adaptive evaluation within an actor–mentor architecture.The methodology describes these as four framework components.Source: §IV · p. 2

A. LLM as planning Actor

  • The Actor processes the task goal, current observations, and prior failed-trial reflection summaries to generate an action sequence.Generated action sequences are stored in memory to support subsequent self-reflection, and trials continue until task completion.Source: A. LLM as planning Actor · pp. 2–3

B. The Short-Long Memory Management

FISRF separates current-trial trajectories and failure reflections from longer-term valuable knowledge. Its mentor retrieves task-relevant context from a fixed, scenario-specific knowledge base and uses it to generate grounded guidance.

B. The Short-Long Memory Management

  • Short-term memory organizes the current trial’s trajectory and failure reflections, while long-term valuable memory retains mentor knowledge and useful autonomous experiences.This hierarchy is designed to reduce prompt cognitive burden and improve memory organization and reflection effectiveness.Source: B. The Short-Long Memory Management · p. 3

C. The RAG-Based Mentor Module

  • The RAG-based Mentor retrieves relevant documents from a scenario-specific knowledge base using the task goal and current trajectory, then generates grounded guidance.The knowledge base is built offline from successful examples with instance-specific details removed and remains fixed during evaluation.Source: C. The RAG-Based Mentor Module · p. 3

D. The Self-Reflection Model

The self-reflection model distills useful experience and failure lessons, then combines them with mentor knowledge to produce a plan for the next trial.

D. The Self-Reflection Model

  • The reflection process has three stages: distilling high-value experiences, extracting failure lessons from long-term memory, and synthesizing a mentor-informed summary for later planning.The resulting insights are stored in short-term memory for the actor to use in subsequent trials.Source: D. The Self-Reflection Model · pp. 3–4
  • SRexp identifies effective interactions within unsuccessful trial trajectories and distills them into reusable experience for later reflection.Its inputs include the task goal and current trajectory; its parameters are primarily instantiated through prompt design.Source: D. The Self-Reflection Model · p. 4
  • SRlvm incrementally updates long-term valuable memory with universal failure lessons from cross-task trajectories corrected before the current episode.It then retrieves the lesson most relevant to the current trajectory and task goal to inform reflection alongside valuable experience.Source: D. The Self-Reflection Model · p. 4
  • SRsum combines the task goal, current trajectory, valuable experience, failure lesson, and key mentor knowledge to generate an improved plan for the next trial.The plan is stored in short-term memory to guide the actor's subsequent planning.Source: D. The Self-Reflection Model · pp. 4–5

E. The Flexible Reflection Process Under Mentor

The flexible reflection process estimates correction difficulty and improvement between attempts, then uses those measures and episode number to select reflection strategies and mentor guidance.

E. The Flexible Reflection Process Under Mentor

  • Correction difficulty is assessed using task completion rate and the rate of attempted-item reach, with the measures defined relative to total sub-goals and items required.The framework bases its flexibility on difficulty of correction rather than difficulty of the task itself.Source: E. The Flexible Reflection Process… · p. 5
  • Improvement magnitude quantifies change in correction difficulty between consecutive attempts on the same task.The previous and current rounds' correction difficulty values are denoted DCt−1 and DCt, respectively.Source: E. The Flexible Reflection Process… · p. 5
  • Reflection strategy selection uses episode number and improvement magnitude: experience summarization triggers in episode 1 or when improvement exceeds 50%, while failure-lesson summarization triggers later when improvement is at most 50%.Mentor guidance is triggered in the stated ZPD conditions: episode 1 with correction difficulty above 0.5, or a later episode with improvement at most 50%.Source: E. The Flexible Reflection Process… · p. 5

V. EXPERIMENTS

The experiments evaluate FISRF in ALFWorld and on real-world robotic platforms. The reported results indicate consistent baseline outperformance, with gains in overall success rates and fine-grained metrics.

V. EXPERIMENTS

  • FISRF is evaluated in the ALFWorld household simulation environment and through real-world robotic experiments.Source: §V · p. 5
  • The experiments report that FISRF consistently outperforms baseline methods.Source: §V · p. 5
  • The reported improvements include overall success rates and fine-grained performance metrics.Source: §V · p. 5

A. Experimental Setup

The evaluation covers all 134 ALFWorld tasks across five categories and five planning epochs, comparing FISRF with four planning and reflection baselines. It measures success, reflection efficiency, and flexibility.

  • The evaluation spans 134 ALFWorld tasks in Put, Clean, Heat, Cool, and Examine, with each task run over five planning epochs.Source: A. Experimental Setup · p. 5
  • FISRF is benchmarked against ProgPrompt, ReAct, Reflexion, and FCRF, representing plan-only, reason-only, reason-reflect, and flexible-reflection approaches.Source: A. Experimental Setup · p. 5
  • Table I reports success rates across ALFWorld task categories, while Table II covers reflection flexibility and efficiency.Source: Table I · p. 6, Table II · p. 6
  • Success Rate measures the proportion of tasks completed by trial end; efficiency uses Reflection Cost, Revision Rate, and Revision Round.Reflection Cost is token cost per successful recovery; Revision Rate is the share of initially failed tasks recovered, and Revision Round is the mean iterations to recovery.Source: A. Experimental Setup · p. 6
  • Flexibility is assessed using the range of reflection tokens and the coefficient of variation.Source: A. Experimental Setup · p. 6

B. Main Results

FISRF improves ALFWorld task success and reflection efficiency relative to baseline methods. It also demonstrates a successful refinement of a failed food-collection task.

  • FISRF reaches 100% success on Put and Cool, gains over 30% on Examine, and improves overall success by more than 4%.Source: B. Main Results · p. 6
  • Figure 5 shows a food-collection task successfully refined after an initial failure using FISRF.Source: Figure 5 · p. 7
  • R increases by over 6.8% and CV by over 8.5%; Rateref improves by more than 22.5%, Costrev decreases by over 25.1%, and Roundrev by over 31.6%.Source: B. Main Results · p. 7

VI. ANALYSIS AND DISCUSSION

The episode-level analysis tracks replanning outcomes for initially failed long-horizon tasks over five episodes. FISRF outperforms baselines and resolves errors with fewer reflection iterations.

A. Episode Analysis of Self-Reflection Process

B. Ablation Study

Ablations show that experience retention, failure-derived lessons, and mentor guidance each contribute to FISRF’s performance. Real-world kitchen manipulation experiments and the conclusion report broader gains while identifying knowledge-base expansion and cross-environment transfer as future work.

B. Ablation Study

  • Without experience retention, overall success rate falls by 7.9% and R decreases by 21.7%; without failure lessons, success falls by 6.3%, CV by 39.7%, and reflection cost rises by 27.5%.The authors link experience retention to reflective adaptability and stable long-horizon planning, and failure-informed knowledge to flexibility and cost-effective recovery.Source: B. Ablation Study · p. 7
  • Removing mentor guidance reduces success rate by 12.6% and increases reflection cost by 48.6%.The ablation identifies mentor guidance as important for performance and efficiency.Source: B. Ablation Study · p. 7

C. Real-World Robotic Experiment

  • On a dual-arm mobile robot, FISRF demonstrates enhanced self-reflection and recovery in real-world kitchen item manipulation tasks.Figure 5 illustrates the experiment.Source: C. Real-World Robotic Experiment · p. 8

VII. CONCLUSIONS

  • Across ALFWorld and real robotic platforms, FISRF shows consistent gains in task success, correction efficiency, and reflection flexibility.The authors identify automatic knowledge-base expansion and transfer of mentor guidance across environments and robot platforms as future work.Source: §VII · p. 8

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