mlsanityData to 14 Oct 2026

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

arXiv 2605.28186 ↗First posted 27 May 2026cs.RO cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

Deep reinforcement learning (DRL) has been shown to achieve high performance on locomotion control tasks in MuJoCo benchmarks such as HalfCheetah, Ant, and Walker2D. However, visualizing the motion structures internally obtained by a trained policy function implemented as a deep neural network remains challenging. It is known from biomechanics and related fields that locomotion control is realized through the repetition of motion phases such as the stance phase and swing phase. In this study, we propose a framework for uncovering latent motion phase structures from trajectories generated by locomotion control policies through interaction with the environment. The proposed method extends the clustering features from state observations alone to augmented features including actions, next states, and next actions, and introduces a method for determining the number of clusters that suppresses self-transitions. Applying the proposed method to three environments -- Ant-v5, HalfCheetah-v5, and Walker2D-v5 -- we successfully identified phase structures with clearer and more regular transition rules than those obtained by the existing method.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 12 references that are in the map. First posted on arXiv 27 May 2026.

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5 of 12 references.

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Same direction, newest first

  1. 2026Uncovering Latent Phase Structures and Branching Logic in Locomotion Policies: A Case Study on HalfCheetah18 Mar

Other references · 4

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  3. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  4. 2016Graying the black box: Understanding DQNscited by 68

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  3. 2026Mind the Phase: Effective Rank and Representation Health in Legged Locomotion
  4. 2024Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning
  5. 2023DecAP: Decaying Action Priors for Accelerated Imitation Learning of Torque-Based Legged Locomotion Policies
  6. 2025Transferable Latent-to-Latent Locomotion Policy for Efficient and Versatile Motion Control of Diverse Legged Robots
  7. 2025Multi-Loco: Unifying Multi-Embodiment Legged Locomotion via Reinforcement Learning Augmented Diffusion
  8. 2024DiffuseLoco: Real-Time Legged Locomotion Control with Diffusion from Offline Datasets