论文标题
学习预测模型，以控制假肢设备
Learning Predictive Models for Ergonomic Control of Prosthetic Devices
论文作者
论文摘要
我们提出了模型可预测性的互动原始图 - 一种机器人学习框架，用于人机协作任务中的辅助运动，该框架明确地说明了对人类肌肉骨骼系统的生物力学影响。首先，我们扩展了相互作用的原始素以启用预测性生物力学：以当前观察结果和预期的机器人控制信号为条件的人类伴侣的未来生物力学状态的预测。反过来，我们利用这种能力在模型预测的控制策略中确定潜在机器人作用的未来人体工程学和生物力学后果。选择最佳控制轨迹，以最大程度地减少对人肌肉骨骼系统的未来物理影响。我们从经验上证明，我们的方法通过根据生物力学成本功能选择的控制动作来最大程度地减少膝盖或肌肉力。实验是在涉及动力假体设备的合成和现实世界实验中进行的。
We present Model-Predictive Interaction Primitives -- a robot learning framework for assistive motion in human-machine collaboration tasks which explicitly accounts for biomechanical impact on the human musculoskeletal system. First, we extend Interaction Primitives to enable predictive biomechanics: the prediction of future biomechanical states of a human partner conditioned on current observations and intended robot control signals. In turn, we leverage this capability within a model-predictive control strategy to identify the future ergonomic and biomechanical ramifications of potential robot actions. Optimal control trajectories are selected so as to minimize future physical impact on the human musculoskeletal system. We empirically demonstrate that our approach minimizes knee or muscle forces via generated control actions selected according to biomechanical cost functions. Experiments are performed in synthetic and real-world experiments involving powered prosthetic devices.
