✨Learning Smooth Time-Varying Linear Policies with an Action Jacobian Penalty

📝 Summary:
This paper proposes using an action Jacobian penalty to remove unrealistic high-frequency signals from reinforcement learning policies without tuning. It introduces a Linear Policy Net architecture to reduce computational overhead, enabling faster convergence and efficient inference for learning ...

🔹 Publication Date: Published on Feb 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18312
• PDF: https://arxiv.org/pdf/2602.18312

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#ReinforcementLearning #MachineLearning #PolicyLearning #DeepLearning #AI

✨GigaWorld-Policy: An Efficient Action-Centered World--Action Model

📝 Summary:
GigaWorld-Policy is an action-centered World-Action Model that significantly improves robotic policy learning. It decouples visual and motion representations, using dual supervision from action prediction and video generation. This allows for 9x faster inference and 7% higher task success rates c...

🔹 Publication Date: Published on Mar 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.17240
• PDF: https://arxiv.org/pdf/2603.17240
• Project Page: https://gigaai-research.github.io/GigaWorld-Policy/
• Github: https://github.com/open-gigaai/giga-world-policy

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#Robotics #MachineLearning #WorldModels #DeepLearning #PolicyLearning