📚 Humanoid Robot Learning Paper Notebooks

📄 Foundational RL (9)

Foundational RL theory and classic algorithms — essential prerequisites for understanding subsequent research

• Proximal Policy Optimization Algorithms (PPO)
• Advantage Weighted Regression (AWR)
• DeepMimic: Example-Guided Deep RL of Physics-Based Character Skills
• AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control
• PHC: Perpetual Humanoid Control for Real-time Simulated Avatars
• ADD: Adversarial Disentanglement and Distillation
• ASE: Adversarial Skill Embeddings for Large-Scale Motion Control
• Learning Smooth Humanoid Locomotion through Lipschitz-Constrained Policies (LCP)
• Understanding Domain Randomization for Sim-to-real Transfer

📄 High Impact Selection (1)

Curated high-impact and milestone papers in humanoid robot learning

🦾 Loco-Manipulation and WBC (5)

• ULTRA: Unified Multimodal Control for Autonomous Humanoid Whole-Body Loco-Manipulation
• OmniXtreme: Breaking the Generality Barrier in High-Dynamic Humanoid Control
• LessMimic: Long-Horizon Humanoid Interaction with Unified Distance Field Representations
• Learning Humanoid End-Effector Control for Open-Vocabulary Visual Loco-Manipulation (HERO)
• VIGOR: Visual Goal-In-Context Inference for Unified Humanoid Fall Safety

🦿 Locomotion (0)

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🦾 Manipulation (0)

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🎮 Teleoperation (0)

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🧭 Navigation (0)

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📡 State Estimation (0)

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🔄 Sim-to-Real (0)

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🖥️ Simulation Benchmark (0)

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🔧 Hardware Design (0)

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🎯 Physics-Based Animation (0)

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🏃 Human Motion (0)

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