✨RLinf-VLA: A Unified and Efficient Framework for VLA+RL Training

📝 Summary:
RLinf-VLA is a unified framework for scalable reinforcement learning training of vision-language-action models, overcoming supervised fine-tuning limitations. It offers a 1.6x-1.8x speedup, supports diverse architectures and algorithms, and shows strong generalization in simulation and on a real ...

🔹 Publication Date: Published on Oct 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.06710
• PDF: https://arxiv.org/pdf/2510.06710
• Project Page: https://rlinf.readthedocs.io/en/latest/
• Github: https://github.com/RLinf/RLinf

🔹 Models citing this paper:
• https://huggingface.co/RLinf/RLinf-math-7B

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#ReinforcementLearning #VLA #Robotics #AIResearch #MachineLearning

✨RoboChallenge: Large-scale Real-robot Evaluation of Embodied Policies

📝 Summary:
RoboChallenge is an online evaluation system for robotic control algorithms, especially VLA models. It enables large-scale, reproducible real-robot testing to survey state-of-the-art models.

🔹 Publication Date: Published on Oct 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.17950
• PDF: https://arxiv.org/pdf/2510.17950

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#Robotics #AI #MachineLearning #EmbodiedAI #RoboticsEvaluation

✨Kinematify: Open-Vocabulary Synthesis of High-DoF Articulated Objects

📝 Summary:
Kinematify is an automated framework that synthesizes high-DoF articulated objects from images or text. It infers kinematic topologies and estimates joint parameters, combining MCTS search with geometry-driven optimization for physically consistent models.

🔹 Publication Date: Published on Nov 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.01294
• PDF: https://arxiv.org/pdf/2511.01294

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#3DModeling #ComputerVision #Robotics #AIResearch #Kinematics

✨Learning Vision-Driven Reactive Soccer Skills for Humanoid Robots

📝 Summary:
A unified reinforcement learning controller directly integrates visual perception and motion control for humanoid soccer robots. It uses extended Adversarial Motion Priors and an encoder-decoder to achieve reactive, coherent, and robust soccer skills in dynamic real-world environments.

🔹 Publication Date: Published on Nov 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.03996
• PDF: https://arxiv.org/pdf/2511.03996
• Project Page: https://humanoid-kick.github.io/

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#HumanoidRobots #ReinforcementLearning #Robotics #ComputerVision #AI

✨OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM

📝 Summary:
OmniVinci is an open-source omni-modal LLM that improves cross-modal understanding for audio, vision, and robotics. It features innovative architecture for better embedding alignment and temporal capture, along with efficient data curation. OmniVinci outperforms competitors while using significan...

🔹 Publication Date: Published on Oct 17

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/omnivinci-enhancing-architecture-and-data-for-omni-modal-understanding-llm
• PDF: https://arxiv.org/pdf/2510.15870
• Project Page: https://nvlabs.github.io/OmniVinci/
• Github: https://github.com/NVlabs/OmniVinci

🔹 Models citing this paper:
• https://huggingface.co/nvidia/omnivinci

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#LLM #MultimodalAI #Robotics #DeepLearning #OpenSource

✨Robot Learning from a Physical World Model

📝 Summary:
PhysWorld enables robots to learn accurate manipulation from AI-generated videos by integrating video generation with physical world modeling. This approach grounds visual guidance into physically executable actions, eliminating the need for real robot data.

🔹 Publication Date: Published on Nov 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07416
• PDF: https://arxiv.org/pdf/2511.07416
• Project Page: https://pointscoder.github.io/PhysWorld_Web/
• Github: https://github.com/PointsCoder/OpenReal2Sim

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#RobotLearning #Robotics #AI #PhysicalModeling #MachineLearning

✨WMPO: World Model-based Policy Optimization for Vision-Language-Action Models

📝 Summary:
WMPO is a pixel-based world-model framework for on-policy VLA reinforcement learning that avoids real-world interaction. It uses pixel predictions aligned with VLA features to boost sample efficiency, performance, self-correction, and generalization in robotic manipulation.

🔹 Publication Date: Published on Nov 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.09515
• PDF: https://arxiv.org/pdf/2511.09515
• Project Page: https://wm-po.github.io/
• Github: https://github.com/WM-PO/WMPO

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#ReinforcementLearning #VLAModels #WorldModels #Robotics #AI

✨AffordBot: 3D Fine-grained Embodied Reasoning via Multimodal Large Language Models

📝 Summary:
AffordBot uses MLLMs and chain-of-thought reasoning for fine-grained 3D embodied reasoning. It predicts affordance elements' location, motion type, and axis in 3D scenes per instructions. It achieves state-of-the-art by projecting 3D elements for 2D MLLMs.

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10017
• PDF: https://arxiv.org/pdf/2511.10017

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#AffordBot #MLLM #EmbodiedAI #3DReasoning #Robotics

✨PAN: A World Model for General, Interactable, and Long-Horizon World Simulation

📝 Summary:
PAN is a general interactable world model that predicts future states through high-quality action-conditioned video simulation. It uses a GLP architecture combining LLM-based latent dynamics with a video diffusion decoder for detailed long-term coherent results enabling reasoning and acting.

🔹 Publication Date: Published on Nov 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.09057
• PDF: https://arxiv.org/pdf/2511.09057

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#WorldModels #AI #Simulation #GenerativeAI #Robotics

✨PhysX-Anything: Simulation-Ready Physical 3D Assets from Single Image

📝 Summary:
PhysX-Anything generates simulation-ready physical 3D assets from single images, crucial for embodied AI. It uses a novel VLM-based model and an efficient 3D representation, enabling direct use in robotic policy learning.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13648
• PDF: https://arxiv.org/pdf/2511.13648
• Project Page: https://physx-anything.github.io/
• Github: https://github.com/ziangcao0312/PhysX-Anything

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Caoza/PhysX-Mobility

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#EmbodiedAI #3DReconstruction #Robotics #ComputerVision #AIResearch

✨MiMo-Embodied: X-Embodied Foundation Model Technical Report

📝 Summary:
MiMo-Embodied is the first cross-embodied foundation model. It achieves state-of-the-art performance in both autonomous driving and embodied AI, demonstrating positive transfer through multi-stage learning and fine-tuning.

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16518
• PDF: https://arxiv.org/pdf/2511.16518
• Github: https://github.com/XiaomiMiMo/MiMo-Embodied

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#FoundationModels #EmbodiedAI #AutonomousDriving #AI #Robotics

✨MobiAgent: A Systematic Framework for Customizable Mobile Agents

📝 Summary:
MobiAgent is a comprehensive mobile agent system designed to improve real-world task execution accuracy and efficiency. It uses MobiMind models, the AgentRR framework, and MobiFlow benchmarking, plus an AI-assisted data collection pipeline. MobiAgent achieves state-of-the-art performance in mobil...

🔹 Publication Date: Published on Aug 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.00531
• PDF: https://arxiv.org/pdf/2509.00531
• Github: https://github.com/IPADS-SAI/MobiAgent/releases/download/v1.0/Mobiagent.apk

🔹 Models citing this paper:
• https://huggingface.co/IPADS-SAI/MobiMind-Grounder-3B
• https://huggingface.co/IPADS-SAI/MobiMind-Decider-7B
• https://huggingface.co/IPADS-SAI/MobiMind-Mixed-7B

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#MobileAgents #AI #DeepLearning #Robotics #Automation