arxiv: 2605.01772 · v1 · submitted 2026-05-03 · 💻 cs.RO · cs.LG

Recognition: no theorem link

Anticipation-VLA: Solving Long-Horizon Embodied Tasks via Anticipation-based Subgoal Generation

Zhilong Zhang , Wenyu Luo , Haonan Wang , Yifei Sheng , Yidi Wang , Hanyuan Guo , Haoxiang Ren , Xinghao Du

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Yuhan Che Tongtong Cao Lei Yuan Yang Yu

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:29 UTC · model grok-4.3

classification 💻 cs.RO cs.LG

keywords vision-language-action modelslong-horizon taskssubgoal generationembodied roboticshierarchical planninganticipation modeladaptive control

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The pith

An anticipation model that recursively generates and updates subgoals lets vision-language-action robots complete long-horizon tasks without error buildup.

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

The paper sets out to show that standard vision-language-action models break down on extended tasks because small mistakes accumulate across fixed steps. It replaces rigid task breakdowns with a model that keeps forecasting the next subgoals from the current state and revises those forecasts as the world changes. This creates a two-level system where the high-level planner feeds adaptable targets to a low-level action executor. If the claim holds, robots could follow natural-language instructions through sequences of dozens of steps in changing conditions. Readers should care because most current embodied agents still cannot sustain reliable performance once a task stretches beyond a few actions.

Core claim

We introduce the Anticipation Model, which adaptively and recursively generates future subgoals. This model continuously adapts as the task unfolds, adjusting future subgoals in response to evolving dynamics and thereby producing more reliable planning paths. We then build Anticipation-VLA as a hierarchical system that uses the anticipation model to produce actionable subgoals, implemented by finetuning a unified multimodal model for high-level planning and pairing it with a goal-conditioned vision-language-action policy for low-level execution.

What carries the argument

The Anticipation Model, which recursively generates and revises future subgoals from the current visual and language state to guide low-level execution.

If this is right

Hierarchical vision-language-action models that use recursive subgoal adaptation achieve higher success rates on long-horizon tasks than methods relying on fixed subtask decomposition.
Continuous adjustment of future subgoals reduces the propagation of execution errors across many steps.
Finetuning a unified multimodal model for subgoal generation produces targets that a separate goal-conditioned policy can follow reliably in both simulation and real robots.
Adaptive planning paths remain effective even when task dynamics change during execution.

Where Pith is reading between the lines

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

The same recursive adjustment mechanism could be tested on sequential decision problems outside robotics, such as multi-step tool use in software agents, to see whether error accumulation is similarly reduced.
If subgoal revision works without full retraining, it suggests that high-level planners need only modest coverage of possible futures rather than exhaustive real-world data.
A natural extension would be to measure how often the model regenerates subgoals in response to sensor noise versus genuine environmental change.

Load-bearing premise

The model can keep producing accurate future subgoals without its own predictions drifting into compounding mistakes, and training on limited data still transfers to unpredictable real-world conditions.

What would settle it

Execute the system on a multi-stage manipulation sequence in a physical environment that includes unexpected object displacements; measure whether subgoal accuracy and task success rate remain above those of fixed-granularity baselines or drop sharply after the first few steps.

Figures

Figures reproduced from arXiv: 2605.01772 by Hanyuan Guo, Haonan Wang, Haoxiang Ren, Lei Yuan, Tongtong Cao, Wenyu Luo, Xinghao Du, Yang Yu, Yidi Wang, Yifei Sheng, Yuhan Che, Zhilong Zhang.

**Figure 1.** Figure 1: Overall architecture of Anticipation-VLA. The anticipation model adaptively outputs multimodal subgoals guided by progress feedback, while a goal-conditioned VLA executes low-level actions. During this process, we maintain a dynamic goal stack enables backtracking and refinement for robust execution. that each subgoal contributes meaningfully to maximizing cumulative reward, while simplifying policy execut… view at source ↗

**Figure 2.** Figure 2: Inference procedure of Anticipation Model. At each inference step, Value Model Vθ first outputs a progress label. If stagnation is detected, Policy Model πθ generates a textual subgoal. Dynamics Model Pθ then predicts the corresponding visual subgoal. Finally, Inverse Dynamics Model P −1 θ verifies whether the actions inferred from the predicted visual transition align with the generated textual subgoal. D… view at source ↗

**Figure 3.** Figure 3: Illustration of Simulated Tasks. Baselines. We compare Anticipation-VLA against several baselines including: (i) π0 (Black et al., 2024a), a VLA model pretrained on large-scale real-world robotic dataset; (ii) UniVLA (Wang et al., 2025), a unified and native multimodal VLA model that with explicit future image generation; (iii) DreamVLA (Zhang et al., 2025c) 1 , a VLA model that integrates comprehensive wo… view at source ↗

**Figure 4.** Figure 4: Task illustration and Comparison Results of real-world experiments. Hardware & Task Design. To evaluate the real-world performance of Anticipation-VLA, we conduct real-world experiments on the Arx-X5 mobile manipulator platform. We design two long-horizon tasks each with a different modality of goal specification, as shown in [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: Ablation Study on Real-World Tasks. Task Results [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗

**Figure 6.** Figure 6: Performance Comparison on Generalization Settings. Task Results [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗

**Figure 7.** Figure 7: Qualitative visualization of generated subgoals. 11 [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗

**Figure 8.** Figure 8: Hardware Illustration of ARX-X5 robotic platform. B.2. Task Design We design two real-world manipulation tasks to evaluate the long-horizon execution for our framework. 1. Rearrange Objects. In this task, the robot is provided with a goal image that specifies the desired final configuration of the tabletop. The scene contains multiple everyday objects, including fruits (e.g., apples and lemons) and utensil… view at source ↗

**Figure 9.** Figure 9: Seen objects for real-world tasks. B.4. Generalization Evaluation Beyond the standard evaluation on the held-out test set, we additionally conduct robustness tests under challenging conditions, including variations in environmental appearance ( [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗

**Figure 10.** Figure 10: Unseen background for real-world tasks. C. Dataset Construction C.1. Hierarchical Subgoal Annotation To train and evaluate the anticipation model with explicit hierarchical supervision, we annotate multi-level subgoals and corresponding language prompts for trajectories from both simulation benchmarks and real-world tasks. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗

**Figure 11.** Figure 11: Uneen objects for real-world tasks. Subgoal Hierarchy Definition. For each trajectory, we define a hierarchical subgoal structure with multiple levels of granularity. Let level h = 0 denote the highest-level task goal (i.e., the original instruction or final target), and larger values of h correspond to increasingly fine-grained subgoals until reaching atomic, directly executable objectives. Each subgoal … view at source ↗

**Figure 12.** Figure 12: Causal mask configurations for four anticipation tasks. (a) Dynamics Model sequence comprises prompt tokens (causal), initial ViT features (full), VAE condition tokens at t=0 (full), current ViT features (full), action tokens (causal), and VAE generation tokens at t=t (noise). (b) Inverse Dynamics Model sequence comprises Prompt (causal), current ViT (full), next ViT (full), and answer tokens (causal). Bo… view at source ↗

**Figure 13.** Figure 13: Execution process of the first simulated task in the simulator VLAbench. Given a high-level instruction, the agent sequentially completes sub-tasks such as using the hammer, driving the nail, placing the tool back, and hanging the picture, illustrating the full task workflow under long-horizon execution. 0->Push 0->Push 1->Pop put the white mug on the left plate and put the yellow and white mug on the rig… view at source ↗

**Figure 14.** Figure 14: Execution process of the second simulated task in the simulator LIBERO. The agent follows the instruction to rearrange objects by sequentially picking up the white mug and placing it on the left plate, followed by moving the yellow-and-white mug to the right plate, demonstrating the complete task workflow in the simulator. 34 [PITH_FULL_IMAGE:figures/full_fig_p034_14.png] view at source ↗

**Figure 15.** Figure 15: Execution process of the first real-world task. Given a goal state specified by a target image, the robot sequentially rearranges multiple objects by picking and placing items such as the apple, spoon, chopstick, and knife into their designated plates, illustrating the complete task workflow in the real-world setting. 35 [PITH_FULL_IMAGE:figures/full_fig_p035_15.png] view at source ↗

**Figure 16.** Figure 16: Execution process of the second real-world task. Given a language instruction to spell a target word, the robot sequentially picks and places letter blocks in the correct order (e.g., “I–C–M–L”), demonstrating the complete task workflow for long-horizon execution in the real-world setting. 36 [PITH_FULL_IMAGE:figures/full_fig_p036_16.png] view at source ↗

read the original abstract

Vision-Language-Action (VLA) models have emerged as a powerful paradigm for embodied intelligence, enabling robots to perform tasks based on natural language instructions and current visual input. However, existing VLA models struggle with long-horizon tasks due to compounding errors. Prior methods decompose tasks into subtasks of fixed granularity, which cannot adapt to the varying complexity of execution states, limiting their robustness in long-horizon tasks. To overcome this, we introduce Anticipation Model, which adaptively and recursively generates future subgoals. This model continuously adapts as the task unfolds, adjusting future subgoals in response to evolving dynamics, facilitating more reliable planning paths. Building on this concept, we propose Anticipation-VLA, a hierarchical VLA model that leverages the anticipation model to generate actionable subgoals that guide VLA policy execution. We implement Anticipation-VLA with finetuning a Unified Multimodal Model (UMM) for high-level subgoal generation and a goal-conditioned VLA policy for low-level action execution. Experiments in both simulated and real-world robotic tasks demonstrate the effectiveness of Anticipation-VLA, highlighting the importance of adaptive and recursive subgoal generation for robust policy execution.

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 paper proposes an adaptive recursive anticipation model for subgoal generation in long-horizon VLA tasks, but the abstract and description give no metrics or comparisons to show it actually works better than fixed decomposition.

read the letter

The one or two things to know: this paper introduces an anticipation model that generates subgoals adaptively and recursively for long-horizon robot tasks, and builds a hierarchical VLA around it. The adaptive part is presented as the key improvement over fixed decomposition methods. The new element is the recursive generation that adjusts subgoals as the task progresses in response to actual dynamics, implemented by finetuning a Unified Multimodal Model for the high-level part and pairing it with a goal-conditioned VLA for low-level actions. This separation of planning and execution is a solid design choice that could help with the compounding error problem in embodied AI. The paper does well at identifying why existing VLA models fail on long tasks and at outlining a practical architecture that tries to mitigate it through anticipation. The description of how the model continuously adapts is clear and logically follows from the problem statement. The soft spots are in the validation. The abstract claims effectiveness in simulated and real-world tasks, but supplies no numbers, no comparisons to prior VLA or decomposition baselines, and no ablations on the anticipation model or recursion levels. Without those, it's impossible to tell if the adaptive mechanism actually reduces errors or if the anticipation model itself starts compounding mistakes when dealing with distribution shifts in real environments. The central assumption that high-level predictions stay reliable across recursion steps lacks any supporting analysis or error propagation checks. This paper is for robotics and AI researchers working on vision-language-action models and hierarchical planning for complex tasks. Someone already experimenting with VLA fine-tuning or subgoal methods would get the most out of the architectural details, even if they need to add their own experiments. It is coherent and addresses a genuine bottleneck, so it deserves a serious referee. The review process would likely push for the missing quantitative evidence and comparisons. I recommend sending it to peer review with instructions to strengthen the experimental section and address potential error accumulation in the anticipation component.

Referee Report

2 major / 2 minor

Summary. The paper claims that standard VLA models suffer from compounding errors on long-horizon tasks because they rely on fixed-granularity task decomposition. It introduces an Anticipation Model that adaptively and recursively generates future subgoals, continuously adjusting them in response to evolving dynamics. This is used to build Anticipation-VLA, a hierarchical architecture that finetunes a Unified Multimodal Model (UMM) for high-level subgoal generation and employs a goal-conditioned VLA policy for low-level action execution. The abstract asserts that experiments in simulated and real-world robotic tasks demonstrate the effectiveness of this adaptive approach.

Significance. If the empirical claims are substantiated, the adaptive recursive subgoal generation could offer a useful architectural improvement for long-horizon embodied tasks by mitigating the rigidity of fixed decompositions. The hierarchical separation of high-level anticipation from low-level execution is a reasonable design choice, and the use of UMM finetuning for planning is a practical implementation route. However, the absence of any quantitative support in the provided description makes it difficult to determine whether the result would meaningfully advance the field beyond existing hierarchical VLA methods.

major comments (2)

[Abstract] Abstract: the assertion that 'Experiments in both simulated and real-world robotic tasks demonstrate the effectiveness of Anticipation-VLA' is unsupported by any reported metrics, baseline comparisons, ablation studies, or error analysis. This directly undermines evaluation of the central claim that adaptive recursive subgoal generation yields more reliable planning paths than fixed-granularity methods.
[Method (Anticipation Model)] The description of the Anticipation Model states that it 'continuously adapts as the task unfolds, adjusting future subgoals in response to evolving dynamics' to avoid compounding errors, yet no analysis of prediction-error propagation across recursion steps, no ablation on prediction horizon, and no comparison of cumulative subgoal deviation versus non-adaptive baselines is supplied. This assumption is load-bearing for the claim of improved robustness.

minor comments (2)

[Abstract] The acronym 'UMM' is introduced without an explicit expansion or citation to prior work on Unified Multimodal Models.
[Abstract] The abstract refers to 'high-level subgoal generation' and 'low-level action execution' but does not clarify the interface or conditioning mechanism between the two levels.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help clarify the presentation of our contributions. We address each major comment below and commit to revisions that strengthen the empirical support for our claims regarding adaptive recursive subgoal generation.

read point-by-point responses

Referee: [Abstract] Abstract: the assertion that 'Experiments in both simulated and real-world robotic tasks demonstrate the effectiveness of Anticipation-VLA' is unsupported by any reported metrics, baseline comparisons, ablation studies, or error analysis. This directly undermines evaluation of the central claim that adaptive recursive subgoal generation yields more reliable planning paths than fixed-granularity methods.

Authors: We agree that the abstract is too concise to convey the quantitative evidence. The full manuscript includes detailed results in Sections 4 and 5, reporting success rates on long-horizon tasks in simulation and real-world settings, direct comparisons to fixed-granularity VLA baselines, ablations isolating the recursive anticipation component, and error analysis across task horizons. We will revise the abstract to include key metrics (e.g., relative success-rate improvements and statistical significance) so that the central claim is immediately supported by evidence. revision: yes
Referee: [Method (Anticipation Model)] The description of the Anticipation Model states that it 'continuously adapts as the task unfolds, adjusting future subgoals in response to evolving dynamics' to avoid compounding errors, yet no analysis of prediction-error propagation across recursion steps, no ablation on prediction horizon, and no comparison of cumulative subgoal deviation versus non-adaptive baselines is supplied. This assumption is load-bearing for the claim of improved robustness.

Authors: The referee correctly identifies that explicit quantification of error propagation and horizon ablations would strengthen the robustness argument. While the current manuscript demonstrates overall task-level gains and qualitative adaptation examples, it does not contain the requested per-recursion error curves or cumulative deviation metrics. We will add these analyses in a new experimental subsection, including (i) prediction-error accumulation plots over recursion depth, (ii) ablation varying the anticipation horizon, and (iii) subgoal-deviation comparisons against non-adaptive baselines. These additions will directly substantiate the load-bearing assumption. revision: yes

Circularity Check

0 steps flagged

No circularity; architectural proposal validated empirically with no self-referential derivations

full rationale

The paper presents Anticipation-VLA as a hierarchical architecture that introduces an Anticipation Model for adaptive recursive subgoal generation, implemented by finetuning a Unified Multimodal Model for high-level planning and a goal-conditioned VLA for low-level execution. No equations, closed-form derivations, or parameter-fitting steps are described that reduce by construction to the inputs. Claims rest on empirical results in simulation and real-world tasks rather than any self-definition, fitted-input prediction, or self-citation chain. The absence of mathematical reduction or load-bearing self-references keeps the derivation self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the assumption that a separate anticipation model can be trained to produce useful subgoals and that the hierarchical split between planning and execution is beneficial; no free parameters or invented physical entities are named in the abstract.

axioms (1)

domain assumption Hierarchical decomposition into high-level subgoal generation and low-level action execution improves robustness for long-horizon tasks
Invoked when the paper builds Anticipation-VLA on top of the anticipation model and a goal-conditioned VLA policy.

invented entities (1)

Anticipation Model no independent evidence
purpose: To adaptively and recursively generate future subgoals that guide policy execution
New component introduced to address limitations of fixed-granularity methods; no independent evidence outside the paper is provided in the abstract.

pith-pipeline@v0.9.0 · 5550 in / 1289 out tokens · 35508 ms · 2026-05-10T15:29:48.859041+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

14 extracted references

[1]

The scene contains multiple everyday objects, including fruits (e.g., apples and lemons) and utensils (e.g., forks and knives), placed on plates of different shapes and colors

Rearrange Objects.In this task, the robot is provided with a goal image that specifies the desired final configuration of the tabletop. The scene contains multiple everyday objects, including fruits (e.g., apples and lemons) and utensils (e.g., forks and knives), placed on plates of different shapes and colors. Given the target image, the robot is require...
[2]

spell the word ICML

Spell Words.In this task, the robot receives a natural language instruction specifying a target word (e.g., “spell the word ICML”). A set of lettered blocks is scattered on the tabletop. The robot must correctly identify the blocks corresponding to the target letters and place them on the table in the correct left-to-right order to form the specified word...
[3]

pick up the block with letter <C>

Recursive subgoal decomposition.Annotators iteratively decompose each goal into a sequence of intermediate subgoals that are achievable from the current state and correspond to meaningful progress toward the final goal. 3.Multimodal labeling.For each subgoal, annotators provide a language prompt describing the intended subgoal (e.g., “pick up the block wi...
[4]

pick up the [object] and place it on/in [location]

Granularity consistency.Subgoals at the same hierarchy level across different trajectories are annotated to maintain consistent semantic abstraction and comparable planning granularity. Annotated Datasets.Our annotated dataset is composed of four distinct parts: •LIBERO (Simulation): 40 expert trajectories sampled from the LIBERO official expert dataset. ...
[6]

**Current goal description **: A text description of the current goal to achieve
[7]

pick up the [object] and place it on/in [location]

**Current goal observation **: An image of the current goal to achieve (may be absent for high-level tasks) Your task is to predict what action the robot should take from the current state to progress toward the current goal. You should output the action directly. Do not include any other output. Inverse Dynamics Model You are a vision-language model with...
[8]

**Current observation **: An image (first image) showing the current state
[9]

pick up the [object] and place it on/in [location]

**Next observation **: An image (second image) showing the next state to reach Your task is to determine the action that was most likely taken to transition from the current observation to the next observation. You should output the action directly. Do not include any other output. Dynamics Model You are now acting as a **world model ** that simulates rob...
[10]

**Init observation **: An image showing the initial state
[11]

**Current observation **: An image showing the robot’s current view
[12]

pick up the [object] and place it on/in [location]

**Action**: A text describing the manipulation to execute Your task is to predict the **next subgoal frame of visual observation after executing the action at the current state **. ### Important notes: - Maintain **visual coherence ** of the scene - Accurately simulate the physical effect of the manipulation action - Keep object positions and states consi...
[13]

**Previous observation **: An image (first image) showing the robot’s previous state 24 Anticipation-VLA: Solving Long-Horizon Embodied Tasks via Anticipation-based Subgoal Generation
[14]

**Current observation **: An image (first image) showing the robot’s current state
[15]

grasp the [object]

**Goal description **: A text description of the goal to achieve Your task is to evaluate the progress status by comparing the two frames and the textual goal descriptions, then classify into one of three categories: **Category 0 (No Progress/Stagnant) **: - The current state shows MINIMAL or NO progress from the previous state toward the subgoal - The ch...