基于锥形控制屏障函数的已学习姿态技能安全执行
Safe Execution of Learned Orientation Skills with Conic Control Barrier Functions
AI总结：
针对从演示学习中动态系统的安全约束不足及忽略姿态维度的问题，提出在SO(3)上学习稳定DS、结合CCBF约束的方法，保障姿态技能在约束区域内安全执行，经仿真和辅助遥操作切割任务验证有效。
摘要：
在从演示中学习（LfD）领域，动态系统（DSs）因能够生成实时运动并到达预定义目标而受到广泛关注。然而，DSs传统的以收敛为中心的行为在安全关键任务中可能存在不足，尤其是那些需要精确复现演示轨迹，或即使存在扰动或人为干预也必须严格遵守约束区域的任务。此外，现有DS研究通常假设演示仅在欧几里得空间中进行，忽略了姿态在各类应用中的关键作用。为解决这些不足，我们提出了一种创新方法，旨在确保已学习的姿态技能在参考轨迹周围的约束区域内安全执行。该方法包括在SO(3)上学习稳定的DS，从专家演示中观察到的变异性提取时变锥形约束，并利用锥形控制屏障函数（CCBF）限制DS的演化以满足约束。我们通过大量仿真评估验证了该方法，并在辅助遥操作场景下的切割技能任务中展示了其有效性。
英文摘要：
In the field of Learning from Demonstration (LfD), Dynamical Systems (DSs) have gained significant attention due to their ability to generate real-time motions and reach predefined targets. However, the conventional convergence-centric behavior exhibited by DSs may fall short in safety-critical tasks, specifically, those requiring precise replication of demonstrated trajectories or strict adherence to constrained regions even in the presence of perturbations or human intervention. Moreover, existing DS research often assumes demonstrations solely in Euclidean space, overlooking the crucial aspect of orientation in various applications. To alleviate these shortcomings, we present an innovative approach geared toward ensuring the safe execution of learned orientation skills within constrained regions surrounding a reference trajectory. This involves learning a stable DS on SO(3), extracting time-varying conic constraints from the variability observed in expert demonstrations, and bounding the evolution of the DS with Conic Control Barrier Function (CCBF) to fulfill the constraints. We validated our approach through extensive evaluation in simulation and showcased its effectiveness for a cutting skill in the context of assisted teleoperation.
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