RA-VLA is presented, a retrieval-augmented VLA framework that integrates behavior-aligned context retrieval with a grounded execution pipeline that facilitates seamless task adaptation while preserving inference efficiency.
Abstract
Vision-Language-Action (VLA) models provide a versatile foundation for general robotic manipulation, yet they exhibit significant brittleness when confronted with novel task distributions. While In-Context Imitation Learning (ICIL) offers a training-free alternative, existing frameworks suffer from an adaptation bottleneck that hinders the effective translation of expert context to executable actions. This failure originates from superficial retrieval mechanisms and an inherent behavioral inertia that anchors the policy to its pre-trained priors. To address these limitations, we present RA-VLA, a retrieval-augmented VLA framework that integrates behavior-aligned context retrieval with a grounded execution pipeline. By enforcing faithful adherence to functional cues within a scalable architecture, RA-VLA facilitates seamless task adaptation while preserving inference efficiency. Our empirical evaluations across the LIBERO benchmark and a real-world UR5e environment demonstrate that RA-VLA achieves superior success rates and computational efficiency, establishing a robust framework for training-free robotic adaptation.
A reliable TTT framework for VLA policies (VANE), where candidate updates are isolated from the live policy, evaluated on subsequent observations, and committed only when supported by future evidence, making adaptation selective and reversible.
H. Ji, Guo-Yang Xia, Luo-Yang Sun et al.· 0 citations
Vision-Language-Action (VLA) policies are commonly adapted to new manipulation settings through additional gradient updates, which limits rapid deployment when task-specific data or compute is scarce. We present ICI-VLA, a training and retrieval framework that equips a text-action VLM with few-shot test-time adaptation through in-context demonstrations. Unlike mainstream VLA designs based on action-specific multimodal fusion, ICI-VLA retains the native text-generation interface. ICI-VLA updates its parameters only during offline training; at inference, the policy remains fixed and conditions action generation on retrieved micro-demonstrations. The framework decomposes long trajectories into short, semantically labeled examples and trains an RD-Encoder with positives mined by Dynamic Time Warping (DTW), aligning the retrieved context with the phase and geometry of the current subtask. We further introduce Target Action Masking, a context-corruption objective designed to reduce direct action copying and increase reliance on the current observation. ICI-VLA reaches average success rates of 97.7% on LIBERO and 60.4% on RoboTwin 2.0, exceeding the highest reported baseline average on RoboTwin 2.0 by 19.3 percentage points. It also achieves 83.2% across four physical tasks. These results indicate that a fixed VLA policy can benefit from conditioning on spatiotemporally aligned demonstrations at test time.
While Vision-Language-Action (VLA) models pretrained on large-scale robot datasets provide a strong foundation for robot manipulation, their performance can degrade when adapted to new tasks with limited task-specific demonstrations. Retrieval offers a practical way to reuse existing demonstrations for data-efficient adaptation, but existing methods often rely on visual similarity, state-action representations, or task-level language matching. These approaches may overlook the hierarchical structure of long-horizon manipulation tasks, where complete task matches are rare but reusable skills are often abundant. To address this challenge, we propose Hierarchical Skill Retrieval (HSR), a retrieval framework for data-efficient VLA adaptation. Specifically, HSR first decomposes a target task into candidate skill sequences. It evaluates each plan based on both semantic plausibility and skill reliability estimated from the prior dataset. The selected decomposition is then used for hybrid retrieval. This combines subtask-level language retrieval with behavior-feature reranking to identify demonstrations that are both semantically relevant and compatible with the target task. Finally, we adapt the policy through a two-stage pretraining and finetuning pipeline, which separates general skill acquisition from task-specific adaptation. Experiments on the LIBERO benchmark and several real-world robot manipulation tasks show that HSR improves the average success rate by 10.3% and 21.3% over the strongest baseline, respectively. These results demonstrate the effectiveness of structured skill-level retrieval for data-efficient VLA adaptation. Videos and code are available at https://hoar012.github.io/HSR-Project.
Haoran Hao, Shahram Najam Syed, Jeff Schneider et al.· 0 citations
The real-robot benchmark demonstrates that StellaVLA can use both human/robot demos and human-to-robot (XR) demos as in-context structured demonstration to help VLA model adapt to OOD tasks.
Siyu Xu, Yunke Wang, Zijian Wang et al.· 0 citations
As Vision-Language-Action (VLA) models continue to scale in the number of parameters, the computational cost and resource requirements for domain-specific fine-tuning have become significant barriers to practical robotic deployment. While Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA (Low-Rank Adaptation) offer a potential solution, they often fail to match the task success rates of their fully fine-tuned counterparts. In this paper, we propose a novel hierarchical VLA architecture that achieves state-of-the-art performance while maintaining high parameter efficiency. Our model decomposes control into a high-level System 2 for instruction-conditioned semantic context encoding—a frozen PaliGemma-3B backbone with 0.12B trainable LoRA parameters and a reactive System 1 for multimodal fusion and action generation. To optimize training efficiency, System 2 takes only a single egocentric image, while System 1 recovers missing context by integrating wrist-view images via ResNet-34 and proprioceptive state history encoded with a single linear projection layer. This information is fused through a Transformer Encoder, and final action trajectories are refined via a Transformer-parameterized conditional flow-matching decoder. To improve task performance, we generate diverse candidates by sampling from N independently initialized Gaussian noise vectors and using different numbers of denoising steps K per sample, and then select the executed action using a Cal-QL (Calibrated Q-Learning)-based critic. Evaluated on the standardized LIBERO benchmark, our proposed model achieved a 98.1% average success rate, outperforming contemporary fully trained models across all task suites. These results demonstrate that strategic architectural design can enable parameter-efficient models to exceed the performance of full-scale fine-tuning, offering a viable path for high-performance robotics under constrained computational resources.
Woo-Kyoung Jeong, Yongwoo Gu, June-sup Yi et al.· 2026 23rd International Conf...· 0 citations
This work examines whether a robot's action representations preserve the semantic structure captured by pretrained encoders and introduces a plug-and-play method that anchors action representations to a semantic manifold while decomposing representations into a shared semantic channel and a private channel, all discarded at inference, leaving the deployed model unchanged.
Yuan Xu, Youheng Shi, Chengyang Li et al.· arXiv.org· 0 citations
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