Accelerating Torque-Based Legged Locomotion Policies with Decaying Action Priors

Core Concepts

A two-stage framework that leverages position-based imitation data and decaying action priors to accelerate the training of torque-based legged locomotion policies, enabling consistent convergence to high-quality gaits.

Abstract

The paper proposes a two-stage framework for training torque-based legged locomotion policies:

Position Policy Training:

The authors first train a position-based policy to acquire imitation data, eliminating the need for expert knowledge to design optimal controllers.
The tracked joint angles from the position policy's simulation are used as the imitation data.

Decaying Action Priors (DecAP):

In the second stage, the authors incorporate decaying action priors to enhance the exploration of torque-based policies.
A PID controller is used to translate the reference imitation angles into torque values, which are then integrated into the torque-based policy's actions with a gradual time-decay factor.
This provides an initial bias to guide the policy's exploration in the torque space, helping it converge to natural gaits.

The results show that the DecAP approach significantly accelerates learning in the torque space, enabling different types of robots to complete the velocity tracking task within 25 minutes of wall-clock time. DecAP consistently outperforms imitation-based approaches and demonstrates the advantages of torque as an action space by comparing a position-based policy to a position-assisted torque-based policy on a quadruped robot.

Stats

The paper does not provide any specific numerical data or statistics to extract. The key results are presented in the form of figures and qualitative comparisons.

Quotes

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Key Insights Distilled From

DecAP

by Shivam Sood,... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2310.05714.pdf

Deeper Inquiries

How can the DecAP framework be extended to incorporate online imitation data collection, enabling the policy to adapt to diverse terrains and external disturbances

To extend the DecAP framework for online imitation data collection, we can implement a parallelized simulation setup where the position-based policy runs concurrently with the torque-based policy training. This setup allows us to collect imitation data online for all possible commands the position-based policy has learned. By continuously updating the imitation data based on the real-time performance of the position-based policy, the torque-based policy can adapt to diverse terrains and external disturbances. Additionally, incorporating exteroceptive data like terrain maps during online data collection can further enhance the adaptability of the policy to different environments. This approach enables the policy to learn from a wider range of scenarios and improve its robustness in real-world applications.

How can the performance-based approach for action priors be implemented to provide targeted assistance to the robot only when it faces challenges, enabling more efficient curriculum learning

Implementing a performance-based approach for action priors in the DecAP framework involves dynamically adjusting the assistance provided to the robot based on its performance and the challenges it faces. By monitoring the robot's behavior and performance metrics during training, we can selectively introduce action priors when the robot encounters difficulties or deviations from the desired behavior. This adaptive assistance mechanism allows for more efficient curriculum learning by focusing on areas where the policy struggles, thereby accelerating the learning process. By incorporating feedback loops that trigger the provision of action priors based on performance indicators, the policy can receive targeted assistance precisely when needed, leading to faster convergence and improved adaptability to varying conditions.

What other techniques, such as meta-learning or hierarchical control, could be combined with the DecAP framework to further improve the sample efficiency and robustness of torque-based legged locomotion policies

Combining the DecAP framework with meta-learning techniques can enhance the sample efficiency and robustness of torque-based legged locomotion policies. Meta-learning algorithms can facilitate the rapid adaptation of the policy to new tasks or environments by leveraging prior knowledge and experience. By meta-learning the learning process itself, the policy can quickly generalize to novel terrains and disturbances, improving its overall performance and adaptability. Additionally, integrating hierarchical control structures can provide a multi-level framework for decision-making, allowing the policy to operate at different levels of abstraction and complexity. This hierarchical approach enables the policy to handle varying levels of detail and control, leading to more efficient and effective locomotion strategies in complex scenarios.