✨Reinforcing Action Policies by Prophesying

📝 Summary:
ProphRL improves Vision-Language-Action policies by overcoming imitation learning limits. It uses Prophet, a learned world model simulator, with tailored reinforcement learning FA-GRPO and FlowScale for data-efficient and stable post-training. This yields significant success gains on benchmarks a...

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20633
• PDF: https://arxiv.org/pdf/2511.20633
• Project Page: https://logosroboticsgroup.github.io/ProphRL/
• Github: https://github.com/LogosRoboticsGroup/ProphRL

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#ReinforcementLearning #ProphRL #WorldModels #Robotics #DeepLearning

✨GigaBrain-0: A World Model-Powered Vision-Language-Action Model

📝 Summary:
GigaBrain-0 is a VLA model that uses world model-generated data to overcome limitations of real robot data, improving cross-task generalization and policy robustness. This boosts real-world performance on complex manipulation tasks.

🔹 Publication Date: Published on Oct 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.19430
• PDF: https://arxiv.org/pdf/2510.19430
• Project Page: https://gigabrain0.github.io/
• Github: https://github.com/open-gigaai/giga-brain-0

🔹 Models citing this paper:
• https://huggingface.co/open-gigaai/GigaBrain-0-3.5B-Base

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#VLAModels #WorldModels #Robotics #AI #MachineLearning

✨WorldVLA: Towards Autoregressive Action World Model

📝 Summary:
WorldVLA unifies VLA and world models, showing mutual enhancement in image understanding and action generation. It addresses autoregressive action prediction errors with an attention mask strategy that significantly improves performance.

🔹 Publication Date: Published on Jun 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.21539
• PDF: https://arxiv.org/pdf/2506.21539
• Project Page: https://github.com/alibaba-damo-academy/WorldVLA
• Github: https://github.com/alibaba-damo-academy/WorldVLA

🔹 Models citing this paper:
• https://huggingface.co/Alibaba-DAMO-Academy/WorldVLA
• https://huggingface.co/jcenaa/WorldVLA-ActionModel-LIBERO-Goal-256
• https://huggingface.co/jcenaa/WorldVLA-ActionModel-LIBERO-10-256

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#AI #MachineLearning #Robotics #ComputerVision #WorldModels

✨Geometrically-Constrained Agent for Spatial Reasoning

📝 Summary:
Geometrically Constrained Agent GCA resolves the semantic to geometric gap in VLMs for spatial reasoning. It uses a formal task constraint to guide the VLM from semantic analysis to constrained tool execution, achieving SOTA performance.

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22659
• PDF: https://arxiv.org/pdf/2511.22659
• Project Page: https://gca-spatial-reasoning.github.io
• Github: https://github.com/gca-spatial-reasoning/gca

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#SpatialReasoning #VLMs #AI #Robotics #DeepLearning

✨GR-RL: Going Dexterous and Precise for Long-Horizon Robotic Manipulation

📝 Summary:
GR-RL improves VLA policies for dexterous long-horizon manipulation. It filters and augments demonstrations, then refines them with RL. This enables unprecedented complex tasks, notably autonomously lacing a shoe.

🔹 Publication Date: Published on Dec 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01801
• PDF: https://arxiv.org/pdf/2512.01801

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#Robotics #ReinforcementLearning #DexterousManipulation #RoboticManipulation #AI

✨VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference

📝 Summary:
VLASH is an asynchronous inference framework for VLAs. It achieves fast accurate and low-latency robotic control by estimating future robot states bridging prediction-execution gaps. This enables VLAs to perform high-precision tasks like ping-pong with significant speedup and reduced latency.

🔹 Publication Date: Published on Nov 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01031
• PDF: https://arxiv.org/pdf/2512.01031
• Github: https://github.com/mit-han-lab/vlash

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#Robotics #VisionLanguageModels #RealTimeAI #AIResearch #MachineLearning

✨OpenREAD: Reinforced Open-Ended Reasoing for End-to-End Autonomous Driving with LLM-as-Critic

📝 Summary:
OpenREAD enhances autonomous driving via end-to-end reinforcement fine-tuning for both reasoning and planning. It uses an LLM critic to quantify open-ended reasoning, achieving state-of-the-art performance by addressing prior limitations.

🔹 Publication Date: Published on Dec 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01830
• PDF: https://arxiv.org/pdf/2512.01830
• Github: https://github.com/wyddmw/OpenREAD

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#AutonomousDriving #LLMs #ReinforcementLearning #AI #Robotics

✨A Hierarchical Framework for Humanoid Locomotion with Supernumerary Limbs

📝 Summary:
A hierarchical control framework enables stable humanoid locomotion with supernumerary limbs. It combines learning-based gait with model-based limb balancing, improving stability and reducing the CoM trajectory Dynamic Time Warping distance by 47%. This decoupled design effectively mitigates dyna...

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00077
• PDF: https://arxiv.org/pdf/2512.00077
• Github: https://github.com/heyzbw/HuSLs

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#Robotics #HumanoidRobotics #Locomotion #ControlSystems #SupernumeraryLimbs

✨SimScale: Learning to Drive via Real-World Simulation at Scale

📝 Summary:
SimScale is a simulation framework synthesizing diverse driving scenarios from logs. Co-training with this data significantly improves autonomous driving robustness and generalization, scaling with simulation data even without new real-world input.

🔹 Publication Date: Published on Nov 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.23369
• PDF: https://arxiv.org/pdf/2511.23369
• Project Page: https://opendrivelab.com/SimScale
• Github: https://github.com/OpenDriveLab/SimScale

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#AutonomousDriving #Simulation #AI #MachineLearning #Robotics

✨Mixture of Horizons in Action Chunking

📝 Summary:
VLA models struggle with a fixed action chunk horizon. The Mixture of Horizons MoH strategy combines different horizons for both global foresight and fine-grained precision. This improves robotic performance, generalizability, and throughput, achieving new state-of-the-art.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19433
• PDF: https://arxiv.org/pdf/2511.19433
• Project Page: https://timsty1.github.io/moh/
• Github: https://github.com/Timsty1/MixtureOfHorizons/tree/main

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#Robotics #AI #MachineLearning #DeepLearning #ReinforcementLearning