Visualizing Latent Phase Structures in Locomotion Policies: A Multi-Environment Study with Temporal Feature Extension

Researchers propose a novel framework for visualizing latent motion phase structures in deep reinforcement learning locomotion policies by extending clustering features beyond state observations to include actions and next states. The method successfully identifies clearer phase transition patterns across three MuJoCo environments, advancing interpretability of neural network-based control policies.

This research addresses a fundamental challenge in deep reinforcement learning: understanding what internal structures neural networks develop when learning locomotion control. While DRL has achieved impressive performance on benchmarks like HalfCheetah and Walker2D, the mechanisms driving these policies remain opaque. The authors recognize that biological locomotion operates through distinct phases—stance and swing—and leverage this insight to decode learned policies.

The innovation lies in augmenting clustering features beyond raw state observations to include actions, next states, and next actions. This temporal extension captures the sequential dependencies inherent in locomotion, revealing clearer transition rules than previous methods. By also introducing a clustering validation approach that suppresses self-transitions, the framework produces more meaningful phase segmentation.

For the AI and robotics communities, this work has significant implications. Interpretable policies enable safer deployment in physical systems, as engineers can verify that learned behaviors match expected biomechanical patterns. Understanding policy internals accelerates debugging and transfer learning across environments. The multi-environment validation across Ant-v5, HalfCheetah-v5, and Walker2D-v5 demonstrates generalizability.

Looking forward, this interpretability framework could extend to other continuous control domains beyond locomotion. The approach may inform policy distillation, where complex learned behaviors are transferred to simpler, more explainable models. Integration with physics-based priors could further enhance both interpretability and robustness in real-world robotics applications.

• →Framework successfully reveals latent phase structures in DRL locomotion policies through temporal feature augmentation.
• →Extended clustering features to include actions and next states improve transition rule clarity over existing methods.
• →Method demonstrates consistent results across three MuJoCo environments, indicating strong generalizability.
• →Improved policy interpretability enables safer deployment and better debugging in robotics applications.
• →Research advances understanding of neural network decision-making in continuous control tasks.