Abstract
Controlling contact forces during interactions is critical for locomotion and manipulation tasks. While sim-to-real reinforcement learning (RL) has succeeded in many contact-rich problems, current RL methods achieve forceful interactions implicitly without explicitly regulating forces. We propose a method for training RL policies for direct force control without requiring access to force sensing. We showcase our method on a whole-body control platform of a quadruped robot with an arm. Such force control enables us to perform gravity compensation and impedance control, unlocking compliant whole-body manipulation. The learned whole-body controller with variable compliance makes it intuitive for humans to teleoperate the robot by only commanding the manipulator, and the robot's body adjusts automatically to achieve the desired position and force. Consequently, a human teleoperator can easily demonstrate a wide variety of loco-manipulation tasks. To the best of our knowledge, we provide the first deployment of learned whole-body force control in legged manipulators, paving the way for more versatile and adaptable legged robots.
Abstract (translated)
在交互过程中控制接触力对于运动和操作任务至关重要。虽然基于模拟-实测强化学习（RL）在许多接触丰富的問題上已经取得了成功，但目前的RL方法在隐式地调节力之外，无法实现有意义的交互。我们提出了一种不需要访问力感测器的直接力控制RL策略的训练方法。我们在四足机器人的全身控制平台上展示了我们的方法。这种力控制使我们能够执行重力补偿和阻尼控制，解锁顺从的全身操作。具有可变顺应性的全身控制器使得人类通过仅命令操作器，就可以轻松地操作机器人，而机器人的身体会自动调整以达到所需的位置和力。因此，人类遥控器可以很容易地展示各种loco-manipulation任务。据我们所知，我们提供了第一个将学习到的全身力控制应用于腿式操作器的部署，为更加多才多艺和适应性强的腿式机器人铺平道路。
URL
https://arxiv.org/abs/2405.01402
