InternVLA-A1.5: Unifying Understanding, Latent Foresight, and Action for Compositional Generalization
Summary
InternVLA-A1.5 integrates pretrained vision-language models with future prediction in latent space to enable efficient robot manipulation with compositional generalization and long-horizon execution, achieving state-of-the-art results on simulation benchmarks.
View Cached Full Text
Cached at: 07/07/26, 06:42 AM
Paper page - InternVLA-A1.5: Unifying Understanding, Latent Foresight, and Action for Compositional Generalization
Source: https://huggingface.co/papers/2607.04988 Authors:
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
Abstract
InternVLA-A1.5 integrates pretrained vision-language models with future prediction in latent space to enable efficient robot manipulation with preserved semantics and long-horizon execution.
Unified models for robot manipulation aim to equip one policy with both the semantic priors of pretrained VLMs and the physical dynamics learned throughfuture prediction. In practice, existing designs tend to erode the semantics of the pretrained backbone, suffer interference among heterogeneous objectives, and learnfuture predictionfrom scratch in pixel space, leaving the dynamics priors of pretrained video generators unexploited. We present InternVLA-A1.5, which builds the policy on a nativeVLM backbonethat keeps training on VQA and subtask prediction, and attaches a lightweightunified expertforcontinuous action generation.Future predictionis recast as alatent-querying problem, where a small set of learnableforesight tokenscondenses the task-relevant future into a compact latent code under the supervision of a frozenpretrained video generation model, so the policy inheritsworld-model dynamics priorswithout ever learning pixel-level generation. The video branch is discarded at inference, keeping real-time control. Pretrained on 1.2Mrobot episodesand 3Mmultimodal samples, InternVLA-A1.5 achieves the best overall results on all six simulation benchmarks. In the real world, the preserved semantics deliver the strongest compositional generalization on held-out instruction bindings, and the two designs together sustain long-horizon execution.
View arXiv pageView PDFProject pageAdd to collection
Get this paper in your agent:
hf papers read 2607\.04988
Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash
Models citing this paper1
#### InternRobotics/InternVLA-A1.5-base Robotics• 3B• Updatedabout 3 hours ago • 6 • 5
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2607.04988 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2607.04988 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation
IntentVLA is a history-conditioned visual-language-action framework that improves robot imitation learning stability by encoding short-horizon intents from visual observations, addressing challenges from partial observability and ambiguous observations. It also introduces AliasBench, an ambiguity-aware benchmark for evaluating such methods.
VisualThink-VLA: Visual Intermediate Reasoning for Effective and Low-Latency Vision-Language-Action Policies
VisualThink-VLA introduces a visual intermediate reasoning framework for vision-language-action policies that preserves spatial precision and dramatically reduces latency compared to text-based reasoning, achieving sub-second inference and state-of-the-art success rates on robot manipulation benchmarks.
AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding
AffordanceVLA introduces a unified framework using structured affordance forecasting as an intermediate representation to improve perception-action mapping in robotic manipulation, leveraging vision-language models and a Mixture-of-Transformer architecture.
From Foundation to Application: Improving VLA Models in Practice
This paper presents LingBot-VLA 2.0, which enhances VLA foundation models for robotics by improving generalization across tasks and embodiments, expanding action space to whole-body degrees of freedom, and incorporating predictive dynamics modeling for better temporal reasoning.
EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies
EventVLA introduces a sparse visual evidence memory framework for long-horizon robotic manipulation, achieving an average success rate improvement of +40% over state-of-the-art memory-augmented VLAs.