PTRL: Prior Transfer Deep Reinforcement Learning for Legged Robots Locomotion

Abstract: In the field of legged robot motion control, reinforcement learning (RL) holds great promise but faces two major challenges: high computational cost for training individual robots and poor generalization of trained models. To address these problems, this paper proposes a novel framework called Prior Transfer Reinforcement Learning (PTRL), which improves both training efficiency and model transferability across different robots. Drawing inspiration from model transfer techniques in deep learning, PTRL introduces a fine-tuning mechanism that selectively freezes layers of the policy network during transfer, making it the first to apply such a method in RL. The framework consists of three stages: pre-training on a source robot using the Proximal Policy Optimization (PPO) algorithm, transferring the learned policy to a target robot, and fine-tuning with partial network freezing. Extensive experiments on various robot platforms confirm that this approach significantly reduces training time while maintaining or even improving performance. Moreover, the study quantitatively analyzes how the ratio of frozen layers affects transfer results, providing valuable insights into optimizing the process. The experimental outcomes show that PTRL achieves better walking control performance and demonstrates strong generalization and adaptability, offering a promising solution for efficient and scalable RL-based control of legged robots.

• The paper introduces Prior Transfer Reinforcement Learning (PTRL), a novel framework combining transfer learning and deep reinforcement learning to efficiently train and transfer locomotion policies for legged robots.
• PTRL significantly reduces training time and computational resources needed for DRL, achieving comparable or better locomotion performance on target robots by leveraging pre-trained policies.
• Extensive experiments on multiple robot platforms like quadruped and humanoid robots validate PTRL's effectiveness in cross-platform generalization and adaptability.

PTRL: Advancing Legged Robot Locomotion through Prior Transfer in Reinforcement Learning

The paper "PTRL: Prior Transfer Deep Reinforcement Learning for Legged Robots Locomotion" addresses the challenges of high computational costs and poor generalization in deep reinforcement learning (DRL) for legged robot locomotion control. It introduces a novel framework called Prior Transfer Reinforcement Learning (PTRL), designed to enhance both the efficiency of the training process and the transferability of learned models across different robotic platforms.

Main Contributions

1. Integration of Transfer Learning and RL: The study innovatively combines transfer learning with reinforcement learning to facilitate rapid training of legged robots. PTRL effectively leverages prior knowledge, accelerating the training process without sacrificing performance. This approach includes a strategic use of layer freezing, inspired by practices in deep learning and natural language processing, allowing for selective layer updates during model transfer.
2. Efficiency in Computational Resources and Time: The proposed method substantially reduces the resources and time required for training when compared to traditional RL techniques. By pre-training a policy using the Proximal Policy Optimization (PPO) algorithm on a source robot and then transferring and fine-tuning this policy on a target robot with partial layer freezing, PTRL maintains performance levels while optimizing the learning process.
3. Experimental Validation and Generalization: Extensive experiments were conducted on multiple robot platforms, including quadruped and humanoid robots. These tests confirmed the method's efficacy in reducing training times while achieving or even surpassing the performance of models trained from scratch. The study further demonstrates strong generalization and adaptability across varied robotic configurations, indicating a significant advancement in scalable DRL-based control systems.

Methodological Insights and Results

The PTRL framework is structured in three distinct stages: pre-training, transfer, and fine-tuning. Initially, a source robot is trained using PPO to achieve policy convergence. The policy network's learned parameters are then transferred to a target robot with specific layers set as non-trainable, allowing only the unfrozen layers to undergo fine-tuning. This layer freezing technique is a pivotal adaptation that underscores PTRL's strength, optimizing layer utilization for enhanced transfer results.

Quantitative analysis demonstrates a nuanced understanding of how different ratios of layer freezing impact the transfer outcome. Results highlight that freezing fewer layers (e.g., only the later network layers) tends to preserve more room for adaptation in the target domain, leading to higher performance gains.

Trained and tested on several robotic forms like the Unitree Go2, MIT Humanoid, and Cassie, PTRL consistently showed reduced training times—approximately 20% faster compared to non-transfer approaches—without compromising on the quality of locomotion. The framework's utility was particularly pronounced when transferring between quadrupedal to humanoid configurations, showcasing its robustness in cross-platform applications.

Implications and Future Directions

The implications of this research are twofold: practical and theoretical. Practically, PTRL provides an efficient RL strategy for companies and researchers engaged in robotic locomotion, offering a more resource-effective methodology aligned with industrial and operational needs. Theoretically, it opens avenues for further refinement of transfer learning techniques in robotics, potentially extending beyond locomotion into other complex control tasks.

Future research could focus on enhancing skill transfer and adaptability in varied terrain and environmental conditions. Additional exploration into multi-skill policy architectures and more sophisticated fine-tuning strategies could further improve PTRL's applicability and robustness, pushing the boundaries of intelligent robotic control systems.

In conclusion, PTRL effectively addresses existing RL limitations in legged robots through thoughtful application of transfer learning principles, indicating a promising direction for further innovations in AI-driven robotic motion control.