用旋轮齿轮设计新型力矩驱动器，提升机器人轻量化高扭矩性能
Cycloidal Quasi-Direct Drive Actuator Designs with Learning-based Torque Estimation for Legged Robotics
- 采用旋轮齿轮与准直驱结合，兼顾高扭矩与轻量化
- 基于神经网络的力矩估计有效缓解仿真到现实的差距
- 适合需要动态响应和自适应控制的足式机器人研发
本文提出一种新型旋轮齿轮准直驱执行器设计，用于足式机器人。旋轮齿轮具有高扭矩密度和机械鲁棒性，相较于传统结构更具优势。通过将旋轮齿轮集成至准直驱框架中，可在保持轻量化的同时显著提升机器人在高扭矩、动态负载任务中的表现。此外，我们开发了一种基于执行器网络的力矩估计框架，有效缓解了因旋轮传动复杂动力学带来的仿真到现实差距。该方法有助于更准确地捕捉执行器动态特性，从而提升强化学习的学习效率、敏捷性和适应能力。
原文摘要 · Abstract (English)
This paper presents a novel approach through the design and implementation of Cycloidal Quasi-Direct Drive actuators for legged robotics. The cycloidal gear mechanism, with its inherent high torque density and mechanical robustness, offers significant advantages over conventional designs. By integrating cycloidal gears into the Quasi-Direct Drive framework, we aim to enhance the performance of legged robots, particularly in tasks demanding high torque and dynamic loads, while still keeping them lightweight. Additionally, we develop a torque estimation framework for the actuator using an Actuator Network, which effectively reduces the sim-to-real gap introduced by the cycloidal drive's complex dynamics. This integration is crucial for capturing the complex dynamics of a cycloidal drive, which contributes to improved learning efficiency, agility, and adaptability for reinforcement learning.
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