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In humanoid robotics, choosing the right components, for example, actuators and gearboxes is crucial. Folks can use planetary and cycloidal gear actuators for their precision and strength, along with Series Elastic and Quasi-Direct Drive actuators for smoother, more natural movements. Advanced designs like the [https://humanoids.wiki/w/MIT_Cheetah MIT Cheetah ] actuator push the boundaries with fast, agile movements. Projects like the SPIN initiative are also key, as they make high-quality actuator technology more accessible, helping the field evolve and improve.

[[Reinforcement Learning ]] Training: Enables parallel environments for fast policy training.

Offers a lightweight open-source alternative, supporting maximal coordinate constraints and easier to work with. The MuJoCo_MPC repository, created by Google DeepMind, is a toolset that combines Model Predictive Control (MPC) with the [[MuJoCo ]] physics engine to create real-time behavior synthesis. With the advanced MJX extension, which uses GPU acceleration, it can simulate multiple environments in parallel. One approach is to try to replicate the techniques detailed in the AMP (Adversarial Motion Priors) paper to achieve agile humanoid behavior. For example, implementing a humanoid get-up sequence, which matches what was described in the AMP research.

Although [[MuJoCo ]] can be slower in single-environment simulations, the MJX extension and its parallel processing potential make it a solid competitor. Compared to enviroments, like NVIDIA's Isaac Gym, MuJoCo might stand out for its extensibility and rapid development. One goal could be to try recreate the walking, running, and getting-up behaviors described in the AMP paper and use them as a foundation for training robust humanoid movements in simulation.