arxiv: 2604.13942 · v1 · submitted 2026-04-15 · 💻 cs.RO

Recognition: unknown

Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection

Zhen Liu , Xinyu Ning , Zhe Hu , Xinxin Xie , Weize Li , Zhipeng Tang , Chongyu Wang , Zejun Yang

show 3 more authors

Hanlin Wang Yitong Liu Zhongzhu Pu

Authors on Pith no claims yet

Pith reviewed 2026-05-10 13:17 UTC · model grok-4.3

classification 💻 cs.RO

keywords long-horizon manipulationvision-language-action modelsdual-system frameworkadaptive planningerror recoveryembodied manipulationrobotic controlclosed-loop reasoning

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The pith

Separating high-level planning from low-level execution enables robust long-horizon robotic manipulation.

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

The paper proposes a dual-system framework that splits high-level semantic reasoning from low-level motor control to handle long-horizon manipulation tasks with partial observability and multi-stage dependencies. A vision-language model agent maintains structured task memory, decomposes goals, verifies outcomes, and corrects errors, while a vision-language-action model executes each sub-task through diffusion-based action generation on filtered observations. Current end-to-end policies fail on these tasks because they lack persistent memory and explicit recovery mechanisms. If the separation and closed-loop interaction work as described, robots could complete complex sequences more reliably in real environments where single observations are incomplete. A sympathetic reader would care because this addresses the brittleness that limits practical deployment of embodied systems.

Core claim

The central claim is that explicitly separating high-level semantic reasoning from low-level motor execution creates a closed loop for memory-aware reasoning and adaptive recovery. The high-level planner, implemented as a VLM-based agentic module, maintains structured task memory and performs goal decomposition, outcome verification, and error-driven correction. The low-level executor, instantiated as a VLA-based visuomotor controller, carries out sub-tasks through diffusion-based action generation conditioned on geometry-preserving filtered observations. Experiments on representative RMBench tasks show this yields a 32.4 percent average success rate versus 9.8 percent for the strongest end-

What carries the argument

The dual-system closed-loop architecture with a VLM-based planner for memory and correction paired with a VLA-based executor for action generation.

If this is right

Success rates on long-horizon tasks rise when structured memory and closed-loop recovery are added to existing visuomotor policies.
Error-driven replanning allows the system to continue after execution failures that defeat single-system baselines.
The separation reduces brittleness in tasks with occlusions and multi-stage dependencies.
Ablation results indicate that both the memory module and the recovery loop contribute measurably to the observed gains.

Where Pith is reading between the lines

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

Hybrid planner-executor designs may scale better than ever-larger end-to-end models when tasks require explicit reflection over extended sequences.
The same split could be tested on navigation or multi-robot coordination problems that share partial-observability challenges.
If the planner remains reliable across different vision-language models, the framework offers a modular route to incorporating future reasoning improvements without retraining the controller.

Load-bearing premise

The VLM-based high-level planner can reliably maintain structured task memory, perform accurate outcome verification, and execute error-driven correction under partial observability and occlusions without introducing additional failures.

What would settle it

Reproducing the RMBench experiments and observing that the dual-system success rate remains at or below the 9.8 percent baseline, or that removing the memory and recovery components produces no performance drop.

read the original abstract

Recent vision-language-action (VLA) systems have demonstrated strong capabilities in embodied manipulation. However, most existing VLA policies rely on limited observation windows and end-to-end action prediction, which makes them brittle in long-horizon, memory-dependent tasks with partial observability, occlusions, and multi-stage dependencies. Such tasks require not only precise visuomotor control, but also persistent memory, adaptive task decomposition, and explicit recovery from execution failures. To address these limitations, we propose a dual-system framework for long-horizon embodied manipulation. Our framework explicitly separates high-level semantic reasoning from low-level motor execution. A high-level planner, implemented as a VLM-based agentic module, maintains structured task memory and performs goal decomposition, outcome verification, and error-driven correction. A low-level executor, instantiated as a VLA-based visuomotor controller, carries out each sub-task through diffusion-based action generation conditioned on geometry-preserving filtered observations. Together, the two systems form a closed loop between planning and execution, enabling memory-aware reasoning, adaptive replanning, and robust online recovery. Experiments on representative RMBench tasks show that the proposed framework substantially outperforms representative baselines, achieving a 32.4% average success rate compared with 9.8% for the strongest baseline. Ablation studies further confirm the importance of structured memory and closed-loop recovery for long-horizon manipulation.

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.

Desk Editor's Note private letter to a colleague

The dual VLM-VLA setup claims a clear jump on long-horizon tasks but the abstract gives no evidence that the planner's corrections actually drive the gains rather than add errors.

read the letter

The main point from this paper is that adding a VLM-based high-level planner with memory and recovery on top of a VLA executor can boost success rates on long-horizon tasks from around 10% to 32% on RMBench. The architecture splits semantic reasoning from motor control, with the planner keeping structured task memory, decomposing goals, verifying outcomes, and triggering replans on failure while the VLA handles diffusion-based actions on filtered observations. This closed loop is presented as the fix for partial observability and multi-stage dependencies that break pure end-to-end VLA policies. The ablations are said to confirm that memory and recovery matter. That combination is a reasonable extension of existing VLM agents and VLA controllers, and the quantitative gap over the strongest baseline is the clearest positive signal in the abstract. The evidence is still thin on several fronts. No details appear on which baselines were used, how many trials were run, what the variance looks like, or how the tasks were defined. The central assumption is that the VLM can reliably verify results and generate useful corrections when scenes are occluded or only partially observed. VLMs often fail exactly at those steps, and if the planner's interventions are neutral or harmful on a non-trivial share of runs, the reported improvement could come from the low-level controller or from task selection instead. The stress-test note on this point holds up from what is shown. This paper is for robotics researchers who already work with VLA models and want a practical way to extend them to longer sequences. Readers who need an architecture diagram and benchmark numbers to start from will get something usable. It has enough of a concrete proposal and reported performance to deserve a full referee rather than a desk reject, even though the current write-up leaves the planner's robustness open to question. I would send it for peer review.

Referee Report

2 major / 2 minor

Summary. The paper proposes Goal2Skill, a dual-system framework for long-horizon robotic manipulation that separates a VLM-based high-level planner (handling structured task memory, goal decomposition, outcome verification, and error-driven correction) from a VLA-based low-level executor (using diffusion-based action generation on geometry-preserving observations). The closed-loop architecture is claimed to enable adaptive replanning and recovery in partially observable tasks. On representative RMBench tasks, it reports 32.4% average success rate versus 9.8% for the strongest baseline, with ablation studies supporting the contributions of memory and closed-loop recovery.

Significance. If the performance gains and attribution to the dual-system design hold after detailed validation, the work would be significant for embodied AI by demonstrating a practical integration of semantic reasoning with visuomotor control to address brittleness in current VLA policies for memory-dependent, multi-stage tasks. The inclusion of ablation studies isolating structured memory and recovery is a strength that provides concrete evidence for the architecture's value.

major comments (2)

[Experiments] Experiments section: The headline result (32.4% vs 9.8% success) is load-bearing for the central claim, yet the manuscript provides insufficient detail on baseline implementations, exact task definitions within RMBench, number of trials per task, variance or statistical significance testing, and whether the low-level VLA executor was held fixed across comparisons. This makes it impossible to confirm that the delta is due to the VLM planner rather than task selection or low-level differences.
[Ablation studies] High-level planner description and ablation studies: The claim that the VLM-based module performs reliable outcome verification and error-driven correction under occlusion and partial observability is an axiom of the framework, but no quantitative metrics (e.g., verification accuracy, fraction of trials where planner interventions increase failures) or failure-case analysis are reported. Without this, the attribution of gains to closed-loop adaptive planning remains unverified and could be explained by the low-level executor alone.

minor comments (2)

[Abstract] Abstract: Implementation specifics (exact VLM and VLA models, observation filtering details) are omitted, which would help readers assess reproducibility even at a high level.
[Figures] Figure captions and task visualizations: Additional detail on what each panel shows (e.g., memory state, verification outcome) would improve clarity for the closed-loop behavior.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comments point by point below and will revise the paper to incorporate the requested details and analyses.

read point-by-point responses

Referee: [Experiments] Experiments section: The headline result (32.4% vs 9.8% success) is load-bearing for the central claim, yet the manuscript provides insufficient detail on baseline implementations, exact task definitions within RMBench, number of trials per task, variance or statistical significance testing, and whether the low-level VLA executor was held fixed across comparisons. This makes it impossible to confirm that the delta is due to the VLM planner rather than task selection or low-level differences.

Authors: We agree that the current manuscript does not provide sufficient experimental details for full verification and replication. In the revised version, we will expand the Experiments section with: detailed descriptions of baseline implementations (including any adaptations made to open-source code), exact task definitions and selection criteria from RMBench, the number of evaluation trials per task, reporting of per-task and aggregate variance (standard deviations), and results of statistical significance testing where appropriate. We will also explicitly state that the low-level VLA executor was held fixed across all comparisons, with differences arising solely from the high-level VLM planner and its memory/closed-loop components. This will strengthen the attribution of gains to the proposed framework. revision: yes
Referee: [Ablation studies] High-level planner description and ablation studies: The claim that the VLM-based module performs reliable outcome verification and error-driven correction under occlusion and partial observability is an axiom of the framework, but no quantitative metrics (e.g., verification accuracy, fraction of trials where planner interventions increase failures) or failure-case analysis are reported. Without this, the attribution of gains to closed-loop adaptive planning remains unverified and could be explained by the low-level executor alone.

Authors: We acknowledge that the manuscript currently lacks direct quantitative metrics on the high-level planner's verification accuracy and the impact of its interventions. In the revision, we will add these metrics (e.g., verification accuracy on held-out trials and the fraction of cases where planner corrections improved vs. degraded outcomes) along with a dedicated failure-case analysis subsection. This will provide concrete evidence that the closed-loop adaptive planning contributes to the observed gains beyond the low-level executor alone. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical framework proposal with direct experimental results

full rationale

The paper describes a dual-system architecture (VLM high-level planner for memory, decomposition, verification and correction; VLA low-level executor for diffusion-based actions) and reports experimental success rates on RMBench tasks. No equations, first-principles derivations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text. The 32.4% vs 9.8% performance figures are presented as measured outcomes of the implemented system rather than quantities forced by construction from inputs. The derivation chain is therefore self-contained empirical description and evaluation.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the unproven effectiveness of the VLM planner in maintaining memory and correcting errors in real robotic settings; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (2)

ad hoc to paper VLM-based agentic module can maintain structured task memory and perform reliable goal decomposition, outcome verification, and error-driven correction
This is the core capability assigned to the high-level planner and is not derived from prior results in the abstract.
domain assumption VLA-based visuomotor controller can execute sub-tasks using diffusion-based action generation on geometry-preserving filtered observations
Assumes the low-level executor functions as described without integration failures.

pith-pipeline@v0.9.0 · 5578 in / 1270 out tokens · 54964 ms · 2026-05-10T13:17:22.780267+00:00 · methodology

discussion (0)

Forward citations

Cited by 1 Pith paper

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

ECHO: Continuous Hierarchical Memory for Vision-Language-Action Models
cs.RO 2026-05 unverdicted novelty 7.0

ECHO organizes VLA experiences into a hierarchical memory tree in hyperbolic space via autoencoder and entailment constraints, delivering a 12.8% success-rate gain on LIBERO-Long over the pi0 baseline.

Reference graph

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