Ensuring safety is of paramount importance in physical human-robot interaction applications. This requires both adherence to safety constraints defined on the system state, as well as guaranteeing compliant behavior of the robot. If the underlying dynamical system is known exactly, the former can be addressed with the help of control barrier functions. The incorporation of elastic actuators in the robot's mechanical design can address the latter requirement. However, this elasticity can increase the complexity of the resulting system, leading to unmodeled dynamics, such that control barrier functions cannot directly ensure safety. In this paper, we mitigate this issue by learning the unknown dynamics using Gaussian process regression. By employing the model in a feedback linearizing control law, the safety conditions resulting from control barrier functions can be robustified to take into account model errors, while remaining feasible. In order to enforce them on-line, we formulate the derived safety conditions in the form of a second-order cone program. We demonstrate our proposed approach with simulations on a two-degree-of-freedom planar robot with elastic joints.

翻译：在人机物理交互应用中，确保安全至关重要。这既需要满足系统状态定义的安全约束，又需要保证机器人的柔顺行为。若底层动力学系统完全已知，前者可通过控制屏障函数实现；而将弹性驱动器集成到机器人机械设计中则可满足后者要求。然而，此类弹性会增大系统复杂性，导致未建模动力学现象，使得控制屏障函数无法直接保障安全。本文通过高斯过程回归学习未知动力学来解决该问题。将模型应用于反馈线性化控制律后，可强化控制屏障函数导出的安全条件，使其能考虑模型误差并保持可行性。为实现在线约束，我们将推导的安全条件表述为二阶锥规划形式。通过在弹性关节两自由度平面机器人上的仿真验证了所提方法的有效性。