用真人运动数据生成可动脚部，让数字人更真实地模拟行走。
Personalised 3D Human Digital Twin with Soft-Body Feet for Walking Simulation
- 基于真人动作捕捉数据生成个性化软体脚部，融合到骨骼模型中。
- 模拟产生的地面反作用力与实测数据接近，关节角度也高度匹配。
- 仅用运动信息训练，即可实现动态精准的行走模拟，适合康复机器人研究。
随着辅助机器人在康复和助行中的广泛应用，对人机交互的深入理解变得愈发重要，尤其依赖仿真环境来分析这些交互。本文提出将基于真人受试者运动捕捉数据生成的个性化软体脚部集成到骨骼模型中，并通过行走控制策略进行训练。通过地面反作用力和关节角结果评估，所生成的软体脚部能够产生与真实测量数据相当的地面反作用力，并紧密跟随裸骨骼模型及参考运动的关节角度变化。该方法展示了仅通过训练时的运动学信息，即可驱动个性化的动态精确人类模型，在仿真中实现逼真行走行为，为康复机器人研究提供了新路径。
原文摘要 · Abstract (English)
With the increasing use of assistive robots in rehabilitation and assisted mobility of human patients, there has been a need for a deeper understanding of human-robot interactions particularly through simulations, allowing an understanding of these interactions in a digital environment. There is an emphasis on accurately modelling personalised 3D human digital twins in these simulations, to glean more insights on human-robot interactions. In this paper, we propose to integrate personalised soft-body feet, generated using the motion capture data of real human subjects, into a skeletal model and train it with a walking control policy. Through evaluation using ground reaction force and joint angle results, the soft-body feet were able to generate ground reaction force results comparable to real measured data and closely follow joint angle results of the bare skeletal model and the reference motion. This presents an interesting avenue to produce a dynamically accurate human model in simulation driven by their own control policy while only seeing kinematic information during training.
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