开源软足模型提升机器人越障能力，仿真验证效果显著。
Soft Adaptive Feet for Legged Robots: An Open-Source Model for Locomotion Simulation
- 用MuJoCo构建可模拟变形的软足数字孪生模型。
- 仿真中软足使机器人越障能力提升，无需修改控制策略。
- 模型开源，适合机器人运动仿真与足部设计研究者使用。
近年来，基于软体机器人和欠驱动原理的人工足部被提出以增强复杂地形下的移动能力。本文利用MuJoCo物理引擎实现了一种用于腿式机器人的自适应软足数字孪生。我们公开发布该软足数字孪生模型，供用户和研究人员探索新的运动方法。研究包含系统建模技术以及涉及的运动学与动力学属性。通过与实物原型的台架实验进行严格对比，在仿真中复现了相同实验，评估指标为足底变形和接触力。该足部模型随后被集成到人形机器人COMAN+的仿真中，替代其原始平足。结果表明，机器人在不改变控制策略的情况下，具备更强的小障碍物通过能力。本研究提供了一套完整的自适应软足建模方法，并通过与先进机器人足部的定性对比，验证了其在双足运动中的有效性。
原文摘要 · Abstract (English)
In recent years, artificial feet based on soft robotics and under-actuation principles emerged to improve mobility on challenging terrains. This paper presents the application of the MuJoCo physics engine to realize a digital twin of an adaptive soft foot developed for use with legged robots. We release the MuJoCo soft foot digital twin as open source to allow users and researchers to explore new approaches to locomotion. The work includes the system modeling techniques along with the kinematic and dynamic attributes involved. Validation is conducted through a rigorous comparison with bench tests on a physical prototype, replicating these experiments in simulation. Results are evaluated based on sole deformation and contact forces during foot-obstacle interaction. The foot model is subsequently integrated into simulations of the humanoid robot COMAN+, replacing its original flat feet. Results show an improvement in the robot's ability to negotiate small obstacles without altering its control strategy. Ultimately, this study offers a comprehensive modeling approach for adaptive soft feet, supported by qualitative comparisons of bipedal locomotion with state of the art robotic feet.
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