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This paper presents a novel framework for learning robust bipedal walking by combining a data-driven state representation with a Reinforcement Learning (RL) based locomotion policy. The framework utilizes an autoencoder to learn a low-dimensional latent space that captures the complex dynamics of bipedal locomotion from existing locomotion data. This reduced dimensional state representation is then used as states for training a robust RL-based gait policy, eliminating the need for heuristic state selections or the use of template models for gait planning. The results demonstrate that the learned latent variables are disentangled and directly correspond to different gaits or speeds, such as moving forward, backward, or walking in place. Compared to traditional template model-based approaches, our framework exhibits superior performance and robustness in simulation. The trained policy effectively tracks a wide range of walking speeds and demonstrates good generalization capabilities to unseen scenarios.
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Learning Terrain-Aware Bipedal Locomotion via Reduced-Dimensional Perceptual Representations
This work introduces a hierarchical strategy for terrain-aware bipedal locomotion that integrates reduced-dimensional perceptual representations to enhance the reinforcement learning (RL)-based high-level (HL) policies for real-time gait generation. Unlike end-to-end approaches, our framework leverages latent terrain encodings via a convolutional variational autoencoder (CNN-VAE) alongside reduced-order robot dynamics, optimizing the locomotion decision process with a compact state. We systematically analyze the impact of latent space dimensionality on learning efficiency and policy robustness. In addition, we extend our method to be history-aware, incorporating sequences of recent terrain observations into the latent representation to improve robustness. To address real-world feasibility, we introduce a distillation method to learn the latent representation directly from depth camera images and provide preliminary hardware validation by comparing simulated and real sensor data. We further validate our framework using the high-fidelity agility robotics (ARs) simulator, incorporating realistic sensor noise, state estimation, and actuator dynamics. The results confirm the robustness and adaptability of our method, underscoring its potential for hardware deployment.
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- Award ID(s):
- 2144156
- PAR ID:
- 10674043
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Control Systems Technology
- ISSN:
- 1063-6536
- Page Range / eLocation ID:
- 1 to 13
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This paper presents a novel framework for learning robust bipedal walking by combining a data-driven state representation with a Reinforcement Learning (RL) based locomotion policy. The framework utilizes an autoencoder to learn a low-dimensional latent space that captures the complex dynamics of bipedal locomotion from existing locomotion data. This reduced dimensional state representation is then used as states for training a robust RL-based gait policy, eliminating the need for heuristic state selections or the use of template models for gait planning. The results demonstrate that the learned latent variables are disentangled and directly correspond to different gaits or speeds, such as moving forward, backward, or walking in place. Compared to traditional template model-based approaches, our framework exhibits superior performance and robustness in simulation. The trained policy effectively tracks a wide range of walking speeds and demonstrates good generalization capabilities to unseen scenarios.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1109/TCST.2026.3664022
