✨V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

📝 Summary:
V-JEPA 2 uses self-supervised learning on web videos and minimal robot data. It excels at video understanding, anticipation, Q&A, and zero-shot robotic planning. This approach yields a powerful world model for physical world planning.

🔹 Publication Date: Published on Jun 11, 2025

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/v-jepa-2-self-supervised-video-models-enable-understanding-prediction-and-planning
• PDF: https://arxiv.org/pdf/2506.09985
• Github: https://github.com/facebookresearch/vjepa2

✨ Datasets citing this paper:
• https://huggingface.co/datasets/ckadirt/vjxla

✨ Spaces citing this paper:
• https://huggingface.co/spaces/vselvarajijay/vjepa2-latent-prediction
• https://huggingface.co/spaces/aavi21458/vjepa2-latent-prediction

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#AI #SelfSupervisedLearning #VideoAI #Robotics #WorldModels

✨ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model

📝 Summary:
ThinkJEPA improves latent world models by combining dense JEPA dynamics with VLM semantic guidance through a dual-temporal pathway. This framework enhances long-horizon hand-manipulation trajectory prediction.

🔹 Publication Date: Published on Mar 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.22281
• PDF: https://arxiv.org/pdf/2603.22281

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#ThinkJEPA #LatentWorldModels #VLM #Robotics #AI

✨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

✨StreamingClaw Technical Report

📝 Summary:
StreamingClaw is a unified framework for real-time streaming video understanding and embodied intelligence. It integrates real-time reasoning, multimodal long-term memory, and proactive interaction, enabling direct control of the physical world.

🔹 Publication Date: Published on Mar 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.22120
• PDF: https://arxiv.org/pdf/2603.22120

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#EmbodiedAI #VideoUnderstanding #RealTimeAI #Robotics #MultimodalAI

✨Pixel-level Scene Understanding in One Token: Visual States Need What-is-Where Composition

📝 Summary:
CroBo is a visual state representation framework that learns what-is-where composition for robotics. It uses global-to-local reconstruction to encode scene element identities and spatial locations in a compact token. This enables tracking scene dynamics for sequential decision making.

🔹 Publication Date: Published on Mar 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13904
• PDF: https://arxiv.org/pdf/2603.13904
• Project Page: https://seokminlee-chris.github.io/CroBo-ProjectPage/
• Github: https://github.com/SeokminLee-Chris/CroBo

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#Robotics #ComputerVision #SceneUnderstanding #AI #StateRepresentation

✨Tex3D: Objects as Attack Surfaces via Adversarial 3D Textures for Vision-Language-Action Models

📝 Summary:
Tex3D is the first framework optimizing 3D adversarial textures to attack vision-language-action models. It significantly degrades robotic manipulation performance in real-world settings, revealing critical vulnerabilities.

🔹 Publication Date: Published on Apr 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01618
• PDF: https://arxiv.org/pdf/2604.01618
• Project Page: https://vla-attack.github.io/tex3d/
• Github: https://github.com/vla-attack/tex3d

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#AdversarialAI #Robotics #VLAmodels #Cybersecurity #ComputerVision

✨Do World Action Models Generalize Better than VLAs? A Robustness Study

📝 Summary:
World Action Models WAMs show superior robustness in robot action planning compared to Vision-Language-Action VLAs. WAMs achieve higher success rates on benchmarks under various perturbations, benefiting from video-based dynamic prediction.

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.22078
• PDF: https://arxiv.org/pdf/2603.22078

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#Robotics #AI #MachineLearning #Robustness #ComputerVision

✨ViVa: A Video-Generative Value Model for Robot Reinforcement Learning

📝 Summary:
ViVa is a video-generative value model for robot reinforcement learning. It estimates values by leveraging pretrained video generators to predict future robot dynamics, moving beyond static observations. This approach improves robot manipulation and generalizes to novel objects.

🔹 Publication Date: Published on Apr 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.08168
• PDF: https://arxiv.org/pdf/2604.08168
• Project Page: https://viva-value-model.github.io/
• Github: https://github.com/GigaAI-research/ViVa

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#Robotics #ReinforcementLearning #GenerativeAI #MachineLearning #AI

✨DeVI: Physics-based Dexterous Human-Object Interaction via Synthetic Video Imitation

📝 Summary:
DeVI enables physically plausible dexterous robot control by leveraging text-conditioned synthetic videos through a hybrid tracking reward that combines 3D and 2D tracking for improved hand-object int...

🔹 Publication Date: Published on Apr 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20841
• PDF: https://arxiv.org/pdf/2604.20841
• Project Page: https://snuvclab.github.io/devi/
• Github: https://github.com/snuvclab/devi

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#Robotics #AI #ComputerVision #HumanRobotInteraction #DeepLearning

✨dWorldEval: Scalable Robotic Policy Evaluation via Discrete Diffusion World Model

📝 Summary:
dWorldEval proposes a scalable robotics policy evaluation method using a discrete diffusion world model. It unifies diverse modalities into a token space, employing a transformer and progress token for success detection. This approach significantly outperforms prior methods, enabling large-scale ...

🔹 Publication Date: Published on Apr 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.22152
• PDF: https://arxiv.org/pdf/2604.22152

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#Robotics #DiffusionModels #WorldModels #AI #MachineLearning