✨RynnVLA-002: A Unified Vision-Language-Action and World Model

📝 Summary:
RynnVLA-002 unifies a Vision-Language-Action and world model, enabling joint learning of environmental dynamics and action planning. This mutual enhancement leads to superior performance, achieving 97.4% success in simulation and a 50% boost in real-world robot tasks.

🔹 Publication Date: Published on Nov 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.17502
• PDF: https://arxiv.org/pdf/2511.17502
• Github: https://github.com/alibaba-damo-academy/RynnVLA-002

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#VisionLanguageAction #WorldModels #Robotics #AI #DeepLearning

✨EvoVLA: Self-Evolving Vision-Language-Action Model

📝 Summary:
EvoVLA is a self-supervised VLA framework tackling stage hallucination in long-horizon robotic manipulation. It uses triplet contrastive learning, pose-based exploration, and memory to prevent shortcuts. EvoVLA significantly improves success, sample efficiency, and reduces hallucination in sim an...

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16166
• PDF: https://arxiv.org/pdf/2511.16166
• Project Page: https://aigeeksgroup.github.io/EvoVLA/
• Github: https://aigeeksgroup.github.io/EvoVLA/

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#Robotics #VisionLanguageAction #SelfSupervisedLearning #AI #DeepLearning

✨VP-VLA: Visual Prompting as an Interface for Vision-Language-Action Models

📝 Summary:
VP-VLA is a dual-system framework that separates high-level task planning from low-level robotic control. It uses visual prompts like bounding boxes to guide the controller, improving spatial precision and robustness in vision-language-action tasks. This approach outperforms existing VLA models.

🔹 Publication Date: Published on Mar 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.22003
• PDF: https://arxiv.org/pdf/2603.22003

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#VisionLanguageAction #Robotics #VisualPrompting #AIResearch #MachineLearning

🔥 RLDX-1 Technical Report

💡 The paper introduces RLDX-1, a general-purpose robotic policy for dexterous manipulation that addresses the limitations of existing vision-language-action models. These models have shown progress in human-like generalist robotic policies but struggle with complex real-world tasks that require broader functional capabilities such as motion awareness, memory-aware decision making, and physical sensing. To overcome this, RLDX-1 uses a Multi-Stream Action Transformer architecture that integrates heterogeneous modalities through modality-specific streams with cross-modal joint self-attention. This architecture is combined with system-level design choices including synthesizing training data for rare manipulation scenarios, learning procedures specialized for human-like manipulation, and inference optimizations for real-time deployment. The results show that RLDX-1 outperforms recent frontier vision-language-action models across both simulation benchmarks and real-world tasks, achieving success rates of 86.8 percent in ALLEX humanoid tasks compared to around 40 percent for other models. This positions RLDX-1 as a promising step toward reliable vision-language-action models for complex and dynamic real-world dexterous manipulation. The method and results demonstrate the ability of RLDX-1 to control a high-degree-of-freedom humanoid robot under diverse functional demands, highlighting its potential for complex real-world tasks.

📅 Published on May 5

🔗 Links:
• arXiv: https://arxiv.org/abs/2605.03269
• PDF: https://arxiv.org/pdf/2605.03269
• Project Page: http://rlwrld.ai/rldx-1
• GitHub: https://github.com/RLWRLD/RLDX-1 ⭐ 75

🤖 Models citing this paper:
• https://huggingface.co/RLWRLD/RLDX-1-PT
• https://huggingface.co/RLWRLD/RLDX-1-FT-ROBOCASA
• https://huggingface.co/RLWRLD/RLDX-1-MT-ALLEX

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📢 By: https://xn--r1a.website/PaperNexus

#RoboticManipulation #DexterousRobotics #VisionLanguageAction #MultiModalLearning #RobotPolicyLearning

🔥 CapVector: Learning Transferable Capability Vectors in Parametric Space for Vision-Language-Action Models

💡 This paper proposes a novel approach called CapVector to improve the performance of vision-language-action models. The problem addressed is that pre-trained models often fail to improve performance and reduce adaptation costs during standard supervised finetuning. Advanced finetuning methods with auxiliary training objectives can improve performance but incur significant computational overhead.

The proposed method decouples the auxiliary training objectives from standard supervised finetuning to enhance model capabilities while reducing computational overhead. This is achieved by training the model to converge on a small-scale task set using two distinct training strategies, resulting in two finetuned models. The parameters difference between the two models is interpreted as capability vectors provided by auxiliary objectives. These vectors are then merged with pre-trained parameters to form a capability-enhanced meta model.

The method also uses a lightweight orthogonal regularization loss to augment standard supervised finetuning, which reduces computational overhead. The results show that the capability vectors are effective and versatile across diverse models, and can generalize to novel environments and embodiments without additional training. The proposed approach achieves performance comparable to auxiliary finetuned baselines with reduced computational overhead, making it a promising solution for improving vision-language-action models.

📅 Published on May 11

🔗 Links:
• arXiv: https://arxiv.org/abs/2605.10903
• PDF: https://arxiv.org/pdf/2605.10903
• Project Page: https://capvector.github.io/
• GitHub: https://github.com/OpenHelix-Team/CapVector ⭐ 26

🤖 Models citing this paper:
• https://huggingface.co/haofuly/capvector_models_collection

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📢 By: https://xn--r1a.website/PaperNexus

#VisionLanguageModels #ParametricSpaceLearning #TransferableCapabilities #VisionLanguageAction #MultimodalLearning

🔥 Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models

💡 The paper presents Qwen-RobotManip, a generalizable Vision-Language-Action foundation model for robotic manipulation that achieves strong generalization through unified alignment across representation, motion, and behavior dimensions. The problem addressed is that robotic manipulation data is heterogeneous, expensive to collect, and narrow in diversity, making it challenging to achieve alignment and scale in training. The authors propose a unified alignment framework that enables large-scale multi-source training, allowing the model to absorb manipulation data at a scale that prior training regimes could not sustain.

The method involves a human-to-robot synthesis pipeline that converts egocentric hand demonstrations into robot trajectories across 15 platforms, and a rigorous curation pipeline that harmonizes heterogeneous datasets. The model is trained on a large pretraining corpus of approximately 38,100 hours, constructed using only open-source datasets and human videos without proprietary data collection.

The results show that Qwen-RobotManip exhibits emergent generalization capabilities, including zero-shot instruction following, robustness to perturbations, reactive error recovery, and cross-embodiment transfer. The model substantially outperforms prior state-of-the-art models, including π0.5, across all out-of-distribution settings, and ranks 1st in RoboChallenge with a 20% relative improvement. The model is also validated on real-robot platforms, including AgileX ALOHA, Franka, UR, and ARX. The paper concludes that Qwen-RobotManip achieves genuine generalization in robotic manipulation, demonstrating the effectiveness of the unified alignment framework and large-scale training approach.

📅 Published on Jun 17

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.17846
• PDF: https://arxiv.org/pdf/2606.17846
• Project Page: https://qwen.ai/blog?id=qwen-robotmanip

📊 Datasets citing this paper:
• https://huggingface.co/datasets/cy0307/awesome-egocentric-atlas

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📢 By: https://xn--r1a.website/PaperNexus

#RobotLearning #FoundationModels #RoboticManipulation #VisionLanguageAction #MultiSourceTraining

🔥 VLA-Corrector: Lightweight Detect-and-Correct Inference for Adaptive Action Horizon

💡 The paper introduces VLA-Corrector, a lightweight framework that improves the robustness of vision-language-action models in contact-rich manipulation tasks. The problem addressed is the limitation of action chunking, where a fixed action horizon can lead to compounding errors due to small local perturbations. To solve this, VLA-Corrector proposes a detect-and-correct mechanism that continuously monitors the visual feature evolution and detects deviations from the predicted trajectory. When a deviation is detected, the system triggers a corrective replanning event, discarding the remaining stale actions and invoking online gradient guidance to replan the actions. This approach induces an adaptive action horizon, preserving long-horizon execution when the current chunk is reliable and invoking short-horizon corrective replanning when execution begins to drift. The method can be integrated into different vision-language-action models without retraining the backbone, and it mitigates the trade-off between execution robustness and policy-call frequency. The results show that VLA-Corrector substantially improves robustness in long-horizon, contact-rich robotic manipulation tasks while preserving much of the efficiency benefit of action chunking.

📅 Published on Jul 2

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.01804
• PDF: https://arxiv.org/pdf/2607.01804
• Project Page: https://zju-omniai.github.io/vla-corrector/

📊 Datasets citing this paper:
• https://huggingface.co/datasets/cy0307/awesome-egocentric-atlas

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📢 By: https://xn--r1a.website/PaperNexus

#VisionLanguageAction #ContactRichManipulation #DetectAndCorrectMechanism #AdaptiveActionHorizon #RobustnessInRobotics

🔥 SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics

💡 The paper introduces SmolVLA, a compact and efficient vision-language-action model designed for affordable and efficient robotics. The problem with existing vision-language-action models is that they are typically very large, with billions of parameters, which leads to high training costs and limited deployability on consumer-grade hardware. These models also rely on academic and industrial datasets, overlooking community-collected data from affordable robotic platforms.

To address this issue, the authors developed SmolVLA, a small and efficient model that can be trained on a single GPU and deployed on consumer-grade GPUs or even CPUs. SmolVLA is designed to be community-driven, using community-collected data from affordable robotic platforms, which reduces both training and inference costs while retaining competitive performance.

The method used to achieve this involves adapting vision-language models into vision-language-action models, but with a much smaller size. The authors also introduced an asynchronous inference stack that decouples perception and action prediction from action execution, allowing for higher control rates with chunked action generation.

The results show that SmolVLA achieves performance comparable to vision-language-action models that are 10 times larger, despite its compact size. The model is evaluated on a range of simulated and real-world robotic benchmarks, and the authors release all code, pretrained models, and training data. Overall, SmolVLA provides a more efficient and affordable solution for robotics, making it possible to deploy vision-language-action models on consumer-grade hardware.

📅 Published on Jun 2, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2506.01844
• PDF: https://arxiv.org/pdf/2506.01844
• Project Page: https://huggingface.co/blog/smolvla

🤖 Models citing this paper:
• https://huggingface.co/lerobot/smolvla_base
• https://huggingface.co/HuggingFaceVLA/smolvla_libero
• https://huggingface.co/jadechoghari/smolvla_metaworld

📊 Datasets citing this paper:
• https://huggingface.co/datasets/lerobot/community_dataset_v3
• https://huggingface.co/datasets/HuggingFaceVLA/community_dataset_v1
• https://huggingface.co/datasets/HuggingFaceVLA/community_dataset_v2

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/HuggingFaceVLA/libero-vla-leaderboard
• https://huggingface.co/spaces/lerobot/robot-learning-tutorial
• https://huggingface.co/spaces/arpitg1304/lerobot_scripts_simplified

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📢 By: https://xn--r1a.website/PaperNexus

#VisionLanguageAction #EfficientRobotics #CompactNeuralNetworks #RoboticsForAll #AffordableAI

🔥 TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM

💡 The paper introduces TurboVLA, a new vision-language-action model that achieves real-time performance at 32 Hz on an RTX 4090 with less than 1 GB VRAM. The conventional approach to vision-language-action models involves using a large language model as the central interface between perception and action, which incurs substantial computational and memory overhead. In contrast, TurboVLA reformulates the conventional pathway as a direct vision-language-action mapping, where visual observations and language instructions are independently encoded and directly exchanged through lightweight bidirectional vision-language interaction. This simplified design constructs task-conditioned representations directly from visual and linguistic features, significantly reducing computational and memory costs.

The TurboVLA model achieves 97.7 percent average success with only 0.2 billion parameters, 31.2 ms inference latency, and 0.9 GB inference VRAM on a consumer-grade RTX 4090, matching or outperforming substantially larger vision-language-action policies. The results establish TurboVLA as a simple and effective alternative to the prevailing language-centric vision-language-action paradigm, offering a new perspective on how vision, language, and action can be connected for efficient robotic manipulation. The code for TurboVLA is available online, making it accessible for further research and development. Overall, the paper presents a significant contribution to the field of vision-language-action modeling, demonstrating the potential for real-time and efficient robotic manipulation using a novel and simplified approach.

📅 Published on Jul 29

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.27205
• PDF: https://arxiv.org/pdf/2607.27205
• Project Page: https://h-embodvis.github.io/TurboVLA/

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📢 By: https://xn--r1a.website/PaperNexus

#VisionLanguageAction #RealTimeAI #EfficientDeepLearning #VisionLanguageInteraction #LowMemoryComputing