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October 3, 2025Open Access

HuMam: Humanoid Motion Control via End-to-End Deep Reinforcement Learning with Mamba

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HuMam achieves consistent improvements in training stability and task performance for humanoid locomotion.
The policy utilizes a six-term reward system to balance factors like posture and body stability.
This framework employs a single-layer Mamba encoder for effective feature fusion and mapping.
HuMam reduces power consumption and torque peaks while demonstrating significant gains over traditional methods.

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This method shows enhanced learning efficiency and stability in humanoid locomotion, indicating better control and energy savings.

Wang et al. (2025) studied this question.

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