论文标题
在无扭矩传感器驱动的类人动物上，符合兼容的两足球运动的SIM到现实转移
Sim-to-Real Transfer of Compliant Bipedal Locomotion on Torque Sensor-Less Gear-Driven Humanoid
论文作者
论文摘要
SIM到现实是一种应对典型的深度强化学习方法所需的大量试验的主流方法。但是，由于现实差距，将仿真培训的政策转移到实际硬件上仍然是一个悬而未决的挑战。特别是，腿部机器人中执行器的特征对SIM到现实的转移有很大影响。有两个挑战：1）高还原比齿轮被广泛用于执行器中，当考虑到背景性在符合下去的关节时，现实差距问题变得尤为明显。 2）实现稳定的两足球运动的困难导致典型的系统识别方法无法充分传递策略。对于这两个挑战，我们提出了1）齿轮的新仿真模型和2）一种可以利用失败尝试的系统识别方法。该方法的有效性通过Biped Robot，Robotis-OP3验证，SIM转移的策略可以在严重的干扰下稳定机器人，并在不使用力和扭矩传感器的情况下在不平坦的表面上行走。
Sim-to-real is a mainstream method to cope with the large number of trials needed by typical deep reinforcement learning methods. However, transferring a policy trained in simulation to actual hardware remains an open challenge due to the reality gap. In particular, the characteristics of actuators in legged robots have a considerable influence on sim-to-real transfer. There are two challenges: 1) High reduction ratio gears are widely used in actuators, and the reality gap issue becomes especially pronounced when backdrivability is considered in controlling joints compliantly. 2) The difficulty in achieving stable bipedal locomotion causes typical system identification methods to fail to sufficiently transfer the policy. For these two challenges, we propose 1) a new simulation model of gears and 2) a method for system identification that can utilize failed attempts. The method's effectiveness is verified using a biped robot, the ROBOTIS-OP3, and the sim-to-real transferred policy can stabilize the robot under severe disturbances and walk on uneven surfaces without using force and torque sensors.
