Reinforcement Learning for Humanoid Robots: Practical Pipeline
Sun, 24 Aug 2025
Designing a stable RL pipeline for humanoid locomotion: sim-to-real gaps, reward shaping, curriculum design, and safety layers.
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Designing a stable RL pipeline for humanoid locomotion: sim-to-real gaps, reward shaping, curriculum design, and safety layers.
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Building a low-latency teleop stack: input device mapping, FK state streaming, inverse kinematics solving, filtering, and safety.
Read More →Sun, 10 Aug 2025
When ROS/ROS2 is too heavy or mismatched, consider these lighter or domain-focused frameworks and the trade-offs involved.
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