October 8, 2025Open Access

Disturbance-Aware Adaptive Compensation in Hybrid Force-Position Locomotion Policy for Legged Robots

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The disturbance-aware adaptive compensation significantly enhances legged robots' adaptability to various payloads and disturbances.
Results indicate that the proposed hybrid force-position locomotion policy improved performance in both simulations and real-world scenarios.
The research employed reinforcement learning methods and validated the efficiency of the new framework through comprehensive testing.
Enhanced adaptability and performance are critical for legged robots operating in uncertain environments with dynamic changes.

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Observational analysis shows improved mobility and adaptability in legged robots, indicating a need for advanced RL-based motion policies.

Zhang et al. (2025) studied this question.

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Observational analysis shows improved mobility and adaptability in legged robots, indicating a need for advanced RL-based motion policies.