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This is a Experimental Study study.

September 24, 2025Open Access

LocoMamba: Vision-Driven Locomotion via End-to-End Deep Reinforcement Learning with Mamba

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LocoMamba achieves higher success rates and returns than state-of-the-art methods with fewer collisions.
The approach uses end-to-end training with Proximal Policy Optimization to optimize policy performance.
Selective state-space models reduce latency and enhance state representation through compact token generation.
The method demonstrates efficient training in challenging terrains with varying obstacle densities and appearances.

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This framework enhances policy training efficiency in simulated environments with dynamic obstacles, suggesting improved generalization capabilities.

Wang et al. (2025) studied this question.

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This framework enhances policy training efficiency in simulated environments with dynamic obstacles, suggesting improved generalization capabilities.