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arxiv: 2509.16615 · v2 · submitted 2025-09-20 · 💻 cs.RO

LLM-Guided Task- and Affordance-Level Exploration in Reinforcement Learning

Jelle Luijkx , Runyu Ma , Zlatan Ajanovi\'c , Jens Kober This is my paper

Pith reviewed 2026-05-18 15:15 UTC · model grok-4.3

classification 💻 cs.RO
keywords reinforcement learninglarge language modelsrobotic manipulationexplorationtask planningaffordancessim-to-real transferpick-and-place
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The pith

LLM-TALE uses large language models to steer reinforcement learning exploration at task and affordance levels while correcting plans online.

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

The paper presents LLM-TALE as a framework that applies large language model planning to make reinforcement learning more sample-efficient for robotic manipulation. It generates plans at the task level to set overall goals and at the affordance level to identify possible object interactions. These plans direct the agent toward meaningful actions rather than random search in large spaces. The method fixes plans that prove physically unworkable during learning itself and tries multiple affordance options without extra human input or reward design. Tests on pick-and-place tasks show higher success rates and better transfer from simulation to real robots.

Core claim

LLM-TALE integrates planning from large language models at both the task level for high-level goals and the affordance level for object interactions to steer reinforcement learning exploration. It corrects suboptimality in generated plans during online learning and explores multimodal affordance-level plans without human supervision or additional reward engineering, yielding improved sample efficiency and success rates on standard pick-and-place benchmarks along with promising zero-shot sim-to-real transfer.

What carries the argument

The LLM-TALE framework, which combines task-level and affordance-level LLM planning with online correction to direct RL exploration toward semantically meaningful actions.

If this is right

  • Robotic agents reach higher success rates on pick-and-place tasks because exploration stays focused on feasible actions.
  • Fewer training samples are needed since the agent avoids unproductive regions of the state-action space.
  • Plans that are semantically plausible yet physically wrong get adjusted automatically as learning proceeds.
  • Multiple interaction options with objects are tested without requiring human-designed rewards or supervision.
  • Policies learned in simulation transfer to real robots with little or no further adjustment.

Where Pith is reading between the lines

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

  • The dual-level planning approach could apply to other manipulation skills such as assembly or tool use by extending the same task and affordance structure.
  • Reducing the need for hand-crafted rewards might allow reinforcement learning to scale to new tasks with less engineering effort.
  • Testing the method in environments with moving objects or higher uncertainty would reveal how well online plan correction handles dynamic changes.

Load-bearing premise

Large language model plans that appear reasonable but are physically infeasible can still be corrected reliably during online reinforcement learning without human supervision or special reward engineering.

What would settle it

An experiment on pick-and-place tasks showing no improvement in sample efficiency or success rate over strong RL baselines that lack LLM guidance would indicate the online correction and dual-level planning do not deliver the claimed benefits.

Figures

Figures reproduced from arXiv: 2509.16615 by Jelle Luijkx, Jens Kober, Runyu Ma, Zlatan Ajanovi\'c.

Figure 1
Figure 1. Figure 1: LLM-guided Task- and Affordance-Level Exploration (LLM-TALE) uses LLMs to generate task-level and affordance [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Detailed visualization of the task planner scheme from Alg. 1, showing the structure of prompts [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: LLM-TALE explores affordance modalities based on [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Simulation tasks (left to right): PickCube, StackCube, PegInsert, TakeLid, OpenDrawer, and PutBox. 0.0 0.2 0.4 0.6 0.8 1.0 Steps ×106 0.0 0.2 0.4 0.6 0.8 1.0 Success Rate A PickCube 0.0 0.2 0.4 0.6 0.8 1.0 Steps ×10 B 6 StackCube 0.0 0.5 1.0 1.5 2.0 Steps ×10 C 6 PegInsert 0.0 0.5 1.0 Steps ×105 0.0 0.5 1.0 Success Rate D TakeLid 0.0 1.5 3.0 Steps ×10 E 5 OpenDrawer 0.0 1.5 3.0 Steps ×10 F 5 PutBox 0.0 0.5… view at source ↗
Figure 5
Figure 5. Figure 5: Top figures (A-C) show evaluation results with comparisons against baselines and LLM-TALE ablations in ManiSkill [33] tasks, while bottom figures show evaluation results in RLBench [32] (D-F) and vision-based (G-H) tasks. 0 1 2 Steps ×106 −0.1 0.0 0.1 Value A PegInsert (TD3) 0 1 2 Steps ×106 −2 0 B PegInsert (PPO) 0 1 2 3 Steps ×105 −1 0 1 C PutBox (TD3) 0 1 2 3 Steps ×105 −1 0 1 D PutBox (PPO) 0 1 2 Steps… view at source ↗
Figure 6
Figure 6. Figure 6: Visualization of value- and uncertainty-based affordance exploration with LLM-TALE for [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Zero-shot sim-to-real experiment for the [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
read the original abstract

Reinforcement learning (RL) is a promising approach for robotic manipulation, but it can suffer from low sample efficiency and requires extensive exploration of large state-action spaces. Recent methods leverage the commonsense knowledge and reasoning abilities of large language models (LLMs) to guide exploration toward more meaningful states. However, LLMs can produce plans that are semantically plausible yet physically infeasible, yielding unreliable behavior. We introduce LLM-TALE, a framework that uses LLMs' planning to directly steer RL exploration. LLM-TALE integrates planning at both the task level and the affordance level, improving learning efficiency by directing agents toward semantically meaningful actions. Unlike prior approaches that assume optimal LLM-generated plans or rewards, LLM-TALE corrects suboptimality online and explores multimodal affordance-level plans without human supervision. We evaluate LLM-TALE on pick-and-place tasks in standard RL benchmarks, observing improvements in both sample efficiency and success rates over strong baselines. Real-robot experiments indicate promising zero-shot sim-to-real transfer. Code and supplementary material are available at llm-tale.github.io.

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

3 major / 2 minor

Summary. The paper introduces LLM-TALE, a framework that integrates large language models for both task-level and affordance-level planning to directly steer exploration in reinforcement learning for robotic manipulation. It claims to correct suboptimal or physically infeasible LLM-generated plans online during learning, while enabling multimodal affordance exploration without human supervision or extra reward engineering. Evaluations on standard pick-and-place RL benchmarks report gains in sample efficiency and success rates over baselines, with additional real-robot experiments showing promising zero-shot sim-to-real transfer.

Significance. If the empirical claims hold under rigorous controls, the work could meaningfully advance sample-efficient RL for robotics by combining LLM commonsense reasoning with online correction mechanisms. This addresses a key limitation of prior LLM-guided RL methods that assume optimal plans or rewards, potentially reducing reliance on manual reward design and supervision in manipulation domains.

major comments (3)
  1. [§4] §4 (Method): The online correction mechanism for LLM plan suboptimality is described at a high level but lacks a precise algorithmic specification or pseudocode for how physical feasibility is verified and corrected during RL episodes without introducing new reward terms or human input; this is load-bearing for the central claim of operating without additional engineering.
  2. [§5.1] §5.1 (Experiments): The paper reports improvements over 'strong baselines' but does not specify the exact baselines, their hyperparameter tuning protocols, or the number of random seeds used for statistical significance; without these, the magnitude of gains in sample efficiency cannot be properly assessed.
  3. [§5.3] §5.3 (Sim-to-real): The zero-shot transfer results are presented as promising, yet the manuscript provides no quantitative metrics on the sim-to-real gap (e.g., success rate drop) or details on domain randomization used in simulation, which weakens the transfer claim.
minor comments (2)
  1. [§3] Notation for affordance-level plans is introduced without a clear formal definition or distinction from task-level plans in the early sections.
  2. [Figure 2] Figure 2 caption does not explicitly label the online correction loop, making it harder to connect the diagram to the textual description.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment point by point below and indicate the planned revisions.

read point-by-point responses

  1. Referee: [§4] §4 (Method): The online correction mechanism for LLM plan suboptimality is described at a high level but lacks a precise algorithmic specification or pseudocode for how physical feasibility is verified and corrected during RL episodes without introducing new reward terms or human input; this is load-bearing for the central claim of operating without additional engineering.

    Authors: We agree that a more precise specification of the online correction would strengthen the presentation. The current manuscript describes the mechanism at a conceptual level, relying on environment feedback to detect infeasibility and adjust plans via the existing RL policy. In the revised version we will add pseudocode that formalizes the verification step (forward rollout using the simulator dynamics to check reachability) and the correction step (re-querying the LLM for alternative affordance-level actions and injecting them into the current episode without new reward shaping or external supervision). revision: yes

  2. Referee: [§5.1] §5.1 (Experiments): The paper reports improvements over 'strong baselines' but does not specify the exact baselines, their hyperparameter tuning protocols, or the number of random seeds used for statistical significance; without these, the magnitude of gains in sample efficiency cannot be properly assessed.

    Authors: We accept that additional experimental details are required for rigorous evaluation. The revised manuscript will explicitly name the baselines (SAC, PPO, and two recent LLM-guided RL methods), describe the hyperparameter tuning procedure (grid search over learning rate, discount factor, and batch size with validation on a held-out task), and report all results as means and standard deviations over 10 independent random seeds together with statistical significance tests. revision: yes

  3. Referee: [§5.3] §5.3 (Sim-to-real): The zero-shot transfer results are presented as promising, yet the manuscript provides no quantitative metrics on the sim-to-real gap (e.g., success rate drop) or details on domain randomization used in simulation, which weakens the transfer claim.

    Authors: We acknowledge that quantitative support for the transfer claim is currently limited. In the revision we will add the specific domain randomization parameters employed (randomization of object mass, friction coefficients, lighting, and camera pose) and report the observed success-rate drop between simulation and real-robot trials, thereby providing a clearer quantification of the sim-to-real gap. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper presents LLM-TALE as an empirical framework that integrates LLM planning at task and affordance levels to steer RL exploration, with online correction of suboptimality and multimodal exploration. Claims rest on experimental evaluations showing improved sample efficiency and success rates on pick-and-place benchmarks plus sim-to-real transfer, rather than any mathematical derivations, equations, or self-referential definitions. No load-bearing steps reduce by construction to fitted parameters or prior self-citations; the central contribution is a practical method validated against external benchmarks and baselines.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The framework rests on assumptions about LLM planning capabilities and the feasibility of online correction; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (2)
  • domain assumption Large language models possess commonsense knowledge and reasoning abilities that can guide exploration toward more meaningful states in RL.
    Stated directly in the abstract when describing recent methods and the motivation for LLM-TALE.
  • domain assumption Suboptimal or physically infeasible LLM plans can be corrected online to produce reliable robotic behavior.
    Central to the method description contrasting with prior approaches that assume optimal plans.

pith-pipeline@v0.9.0 · 5726 in / 1438 out tokens · 60803 ms · 2026-05-18T15:15:10.546329+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. SYMBOLIZER: Symbolic Model-free Task Planning with VLMs

    cs.RO 2026-04 unverdicted novelty 6.0

    SYMBOLIZER grounds symbolic states from images via VLMs using only lifted predicates and solves long-horizon tasks with goal-count and width-based heuristic search, outperforming direct VLM planning and matching VLM-h...

Reference graph

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