Title: Safe Learning-Based Control of Elastic Joint Robots via Control Barrier Functions Show Chinese title

Title: 基于安全学习的弹性关节机器人控制方法研究：通过控制屏障函数

Abstract: 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. Show Chinese abstract

Abstract: 确保安全性在物理人机交互应用中至关重要。 这要求既要遵守系统状态定义的安全约束，又要保证机器人的合规行为。 如果底层动态系统已知，则前者可以通过控制屏障函数来解决。 在机器人机械设计中采用弹性致动器可以解决后一需求。 然而，这种弹性会增加系统的复杂性，导致未建模的动态特性，从而使得控制屏障函数无法直接确保安全性。 本文中，我们通过使用高斯过程回归学习未知动态来缓解这一问题。 通过在反馈线性化控制律中使用该模型，由控制屏障函数产生的安全条件可以得到鲁棒化处理，以考虑模型误差，同时保持可行性。 为了在线实施这些安全条件，我们将导出的安全条件表述为二阶锥规划的形式。 我们通过具有弹性关节的两自由度平面机器人仿真展示了所提出的方法。