Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning (2408.14472v1)

Abstract: Humanoid robots, with their human-like skeletal structure, are especially suited for tasks in human-centric environments. However, this structure is accompanied by additional challenges in locomotion controller design, especially in complex real-world environments. As a result, existing humanoid robots are limited to relatively simple terrains, either with model-based control or model-free reinforcement learning. In this work, we introduce Denoising World Model Learning (DWL), an end-to-end reinforcement learning framework for humanoid locomotion control, which demonstrates the world's first humanoid robot to master real-world challenging terrains such as snowy and inclined land in the wild, up and down stairs, and extremely uneven terrains. All scenarios run the same learned neural network with zero-shot sim-to-real transfer, indicating the superior robustness and generalization capability of the proposed method.

• The paper introduces DWL, a novel end-to-end RL framework that achieves zero-shot sim-to-real transfer for humanoid locomotion in challenging environments.
• It integrates an encoder-decoder architecture with PPO to effectively denoise sensory data and enhance state estimation.
• Empirical tests confirm that DWL allows robots to master diverse terrains—from stairs to snowy surfaces—without the need for manual tuning.

Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning

The paper "Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning" introduces a novel approach for controlling humanoid robots, facilitating robust navigation across various complex terrains. The authors propose Denoising World Model Learning (DWL) as an end-to-end reinforcement learning (RL) framework which enables superior adaptability and performance in real-world scenarios, hitherto not achieved by existing techniques.

Overview and Contributions

The primary contribution of this work is the development and implementation of DWL, a framework that integrates model-free reinforcement learning with a novel denoising mechanism to bridge the sim-to-real gap. The authors achieve several key advancements, summarized as follows:

1. Real-World Performance: The paper provides empirical evidence demonstrating that DWL enables humanoid robots to robustly master varied and complex terrains. These include snowy environments, inclined surfaces, stairs, and highly uneven terrains, all using the same learned neural network policy without any manual re-tuning.
2. End-to-End Learning: The proposed DWL framework operates in an end-to-end manner, leveraging an encoder-decoder architecture to effectively learn and denoise state representations from partially observed noisy sensory data.
3. First Demonstration: This work implements the first zero-shot sim-to-real transfer for humanoid robot locomotion in complex terrains. The experimental results showcase the humanoid robot's capability to perform a variety of locomotion tasks robustly under different real-world conditions.
4. Active Ankle Control: The introduction of active 2-DoF ankle control, absent in prior RL-based studies, significantly augments the robot's adaptability and stability. This mechanism is validated as crucial for maintaining balance on irregular and inclined surfaces.

Methodological Details

Denoising World Model Learning (DWL)

The core of DWL lies in its ability to mitigate the noise inherent in real-world sensory observations and partial observability. This is achieved through a recurrent encoder-decoder structure:

• Encoder-Decoder Architecture: The proposed framework encodes historical noisy observations into a compact latent state using a GRU-based encoder. The latent state is then decoded to estimate the robot's true state, compensating for various noises such as environmental, dynamics, sensory, and masking noise. This process is mathematically formulated and optimized using a deterministic loss function.
• Integration with Reinforcement Learning: The policy optimization within DWL employs Proximal Policy Optimization (PPO), with an asymmetric actor-critic architecture. The actor utilizes the denoised latent state to determine actions, while the critic leverages privileged information for value estimation, thereby enhancing training efficiency.

Domain Randomization

To ensure robustness against the sim-to-real transfer, the authors employed extensive domain randomization. This involves perturbations in simulation parameters, creating a range of environmental and dynamic conditions that the policy must adapt to. Parameters randomized include friction coefficients, joint positions, velocities, and sensor delays.

Reward Formulation

The reward function integrates multiple components to promote robust locomotion:

• Velocity tracking rewards,
• Periodic rewards based on foot forces and velocities,
• Foot trajectory rewards using quintic polynomial interpolation,
• Regularization terms to maintain stability and minimize energy consumption.

Experimental Validation

Indoor and Outdoor Trials

The humanoid robot was subject to rigorous testing across various surfaces and conditions:

• Indoor Experiments: Included traversing slopes, stair ascent and descent, and walking on irregular terrain. The success rates of DWL surpassed traditional PPO approaches significantly, particularly in complex settings.
• Outdoor Experiments: The DWL framework demonstrated robust performance on surfaces such as snow and brick roads, maintaining stability and efficient locomotion without any pre-tuning.

Robustness Testing

The robot's capability to handle additional load, disturbances, and varying terrain was thoroughly evaluated. Key findings include successful mass displacement handling, the ability to push heavy loads, and robust push recovery on flat and sloped terrains.

Implications and Future Directions

The research presents significant implications for both theoretical advancements and practical applications in the field of humanoid robotics. The ability to achieve zero-shot sim-to-real transfer suggests substantial progress toward deploying humanoid robots in real-world applications, ranging from disaster response to service robots in domestically challenging environments.

Future work could explore the integration of visual feedback to further enhance adaptability and navigation capabilities, reducing reliance on proprioceptive sensors alone. Additionally, extending DWL to other forms of locomotion and manipulation tasks could broaden its applicability and impact.

In conclusion, the DWL framework marks a commendable step forward in humanoid robot locomotion, demonstrating remarkable robustness and adaptability. This work not only showcases the potential of reinforcement learning in real-world robotics but also sets a benchmark for future research in the field.