Paper ID: 2410.16591 • Published Oct 22, 2024
Cycloidal Quasi-Direct Drive Actuator Designs with Learning-based Torque Estimation for Legged Robotics
Alvin Zhu, Yusuke Tanaka, Fadi Rafeedi, Dennis Hong
TL;DR
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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.