使用带参数偏置的循环神经网络实现柔性肌肉骨骼手的物体识别、动态接触仿真、检测与控制
Object Recognition, Dynamic Contact Simulation, Detection, and Control of the Flexible Musculoskeletal Hand Using a Recurrent Neural Network with Parametric Bias
AI总结：
针对柔性肌肉骨骼手难建模、状态易变及多任务需独立网络的问题，提出用带参数偏置的循环神经网络获取其传感器状态方程的方法，可实现多任务集成并应对状态变化，在Musashi手上验证了有效性。
摘要：
柔性肌肉骨骼手难以建模，且其模型会因随时间的劣化、初始化不可复现等问题不断变化。此外，在利用该手进行物体识别、接触检测和接触控制时，理想方案是不使用为每个任务单独训练的神经网络，而仅采用一个集成网络。为此，我们提出一种利用带参数偏置的循环神经网络获取肌肉骨骼手传感器状态方程的方法。通过使用该网络，该手能够实现对抓取物体的识别、接触仿真、检测与控制，还可通过更新参数偏置来应对随时间的劣化、初始化不可复现等问题。我们将该研究应用于肌肉骨骼人形机器人Musashi的手部，验证了其有效性。
英文摘要：
The flexible musculoskeletal hand is difficult to modelize, and its model can change constantly due to deterioration over time, irreproducibility of initialization, etc. Also, for object recognition, contact detection, and contact control using the hand, it is desirable not to use a neural network trained for each task, but to use only one integrated network. Therefore, we develop a method to acquire a sensor state equation of the musculoskeletal hand using a recurrent neural network with parametric bias. By using this network, the hand can realize recognition of the grasped object, contact simulation, detection, and control, and can cope with deterioration over time, irreproducibility of initialization, etc. by updating parametric bias. We apply this study to the hand of the musculoskeletal humanoid Musashi and show its effectiveness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
