From Noise to Intent: Anchoring Generative VLA Policies with Residual Bridges

• URL: http://arxiv.org/abs/2604.21391v1
• Date: Thu, 23 Apr 2026 07:59:26 GMT
• Title: From Noise to Intent: Anchoring Generative VLA Policies with Residual Bridges
• Abstract summary: ResVLA is an architecture that shifts the paradigm to "Refinement-from-Noise-Intent"<n>It focuses on refining local dynamics via a residual diffusion bridge.<n>It demonstrates strong performance in real-world robot experiments.
• Score: 39.128014667094014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bridging high-level semantic understanding with low-level physical control remains a persistent challenge in embodied intelligence, stemming from the fundamental spatiotemporal scale mismatch between cognition and action. Existing generative VLA policies typically adopt a "Generation-from-Noise" paradigm, which disregards this disparity, leading to representation inefficiency and weak condition alignment during optimization. In this work, we propose ResVLA, an architecture that shifts the paradigm to "Refinement-from-Intent." Recognizing that robotic motion naturally decomposes into global intent and local dynamics, ResVLA utilizes spectral analysis to decouple control into a deterministic low-frequency anchor and a stochastic high-frequency residual. By anchoring the generative process on the predicted intent, our model focuses strictly on refining local dynamics via a residual diffusion bridge. Extensive simulation experiments show that ResVLA achieves competitive performance, strong robustness to language and robot embodiment perturbations, and faster convergence than standard generative baselines. It also demonstrates strong performance in real-world robot experiments.

Related papers

• PedestrianDiffusion: Multimodal Generative Denoising and Dense State Estimation for Inertial Navigation [1.8620637029128544]
  PedestrianDiffusion is a generative framework that reformulates dense 6D state estimation as a continuous conditional denoising process.<n>It achieves state-of-the-art performance, exhibiting unprecedented robustness to impulse perturbations and coupled 6D kinematic drift.<n>This work provides a rigorous algorithmic blueprint for next-generation Neural Inertial Measurement Units (N-IMUs)
  arXiv  Detail & Related papers  (2026-07-03T14:04:30Z)
• Think Less, Act Early: Reinforced Latent Reasoning with Early Exit in Vision-Language-Action Models [3.4519796338615225]
  We propose a novel Vision-Language-Action (VLA) framework that models reasoning as a sequence of unobservable latent variables.<n>We introduce a Reinforcement Learning-based Denoising mechanism that treats latent state generation as a sequential decision process.<n>Experiments on embodied decision benchmarks demonstrate that AVA-VLA achieves a 6x inference speedup over explicit CoT methods.
  arXiv  Detail & Related papers  (2026-06-13T04:16:18Z)
• Libra-VLA: Achieving Learning Equilibrium via Asynchronous Coarse-to-Fine Dual-System [15.427164345561417]
  Vision-Language-Action (VLA) models are a promising paradigm for generalist robotic manipulation.<n>We introduce Libra-VLA, a novel Coarse-to-Fine Dual-System VLA architecture.<n>We show that our approach offers a scalable, robust, and responsive solution for open-world manipulation.
  arXiv  Detail & Related papers  (2026-04-27T19:02:46Z)
• Unmasking the Illusion of Embodied Reasoning in Vision-Language-Action Models [28.981226513192535]
  Recent Vision-Language-Action (VLA) models report impressive success rates on standard robotic benchmarks.<n>Recent evidence suggests a systematic misalignment between standard benchmark success and true embodied reasoning.<n>We introduce BeTTER, a diagnostic Benchmark for Testing True Embodied Reasoning in robotic policies.
  arXiv  Detail & Related papers  (2026-04-20T09:25:30Z)
• AnchorRefine: Synergy-Manipulation Based on Trajectory Anchor and Residual Refinement for Vision-Language-Action Models [60.04879435087352]
  Vision-language-action (VLA) policies generate actions within a single unified space.<n>We propose AnchorRefine, a hierarchical framework that factorizes VLA action modeling into trajectory anchor and residual refinement.<n> Experiments on LIBERO, CALVIN, and real-robot tasks demonstrate that AnchorRefine consistently improves both regression-based and diffusion-based VLA backbones.
  arXiv  Detail & Related papers  (2026-04-20T04:25:24Z)
• STRONG-VLA: Decoupled Robustness Learning for Vision-Language-Action Models under Multimodal Perturbations [26.063335767640083]
  We propose STRONG-VLA, a decoupled fine-tuning framework for Vision-Language-Action (VLA) models.<n>In Stage I, the model is exposed to a curriculum of multimodal perturbations with increasing difficulty.<n>In Stage II, the model is re-aligned with clean task distributions to recover execution fidelity while preserving robustness.<n>Experiments on the LIBERO benchmark show that STRONG-VLA consistently improves task success rates across multiple VLA architectures.
  arXiv  Detail & Related papers  (2026-04-11T06:37:47Z)
• Global Prior Meets Local Consistency: Dual-Memory Augmented Vision-Language-Action Model for Efficient Robotic Manipulation [95.89924101984566]
  We introduce OptimusVLA, a dual-memory VLA framework with Global Prior Memory (GPM) and Local Consistency Memory (LCM)<n>GPM replaces Gaussian noise with task-level priors retrieved from semantically similar trajectories.<n>LCM injects a learned consistency constraint that enforces temporal coherence and smoothness of trajectory.
  arXiv  Detail & Related papers  (2026-02-22T15:39:34Z)
• OmniVL-Guard: Towards Unified Vision-Language Forgery Detection and Grounding via Balanced RL [63.388513841293616]
  Existing forgery detection methods fail to handle the interleaved text, images, and videos prevalent in real-world misinformation.<n>To bridge this gap, this paper targets to develop a unified framework for omnibus vision-language forgery detection and grounding.<n>We propose textbf OmniVL-Guard, a balanced reinforcement learning framework for omnibus vision-language forgery detection and grounding.
  arXiv  Detail & Related papers  (2026-02-11T09:41:36Z)
• FASTer: Toward Efficient Autoregressive Vision Language Action Modeling via Neural Action Tokenization [61.10456021136654]
  We introduce FASTer, a unified framework for efficient and general robot learning.<n>FASTerVQ encodes action chunks as single-channel images, capturing global-temporal dependencies while maintaining a high compression ratio.<n>FASTerVLA builds on this tokenizer with block-wise autoregressive decoding and a lightweight action expert, achieving both faster inference and higher task performance.
  arXiv  Detail & Related papers  (2025-12-04T16:21:38Z)
• ReCoM: Realistic Co-Speech Motion Generation with Recurrent Embedded Transformer [58.49950218437718]
  We present ReCoM, an efficient framework for generating high-fidelity and generalizable human body motions synchronized with speech.<n>The core innovation lies in the Recurrent Embedded Transformer (RET), which integrates Dynamic Embedding Regularization (DER) into a Vision Transformer (ViT) core architecture.<n>To enhance model robustness, we incorporate the proposed DER strategy, which equips the model with dual capabilities of noise resistance and cross-domain generalization.
  arXiv  Detail & Related papers  (2025-03-27T16:39:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.