Generating on-purpose impacts with rigid robots is challenging as they may lead to severe hardware failures due to abrupt changes in the velocities and torques. Without dedicated hardware and controllers, robots typically operate at a near-zero velocity in the vicinity of contacts. We assume knowing how much of impact the hardware can absorb and focus solely on the controller aspects. The novelty of our approach is twofold: (i) it uses the task-space inverse dynamics formalism that we extend by seamlessly integrating impact tasks; (ii) it does not require separate models with switches or a reset map to operate the robot undergoing impact tasks. Our main idea lies in integrating post-impact states prediction and impact-aware inequality constraints as part of our existing general-purpose whole-body controller. To achieve such prediction, we formulate task-space impacts and its spreading along the kinematic tree of a floating-base robot with subsequent joint velocity and torque jumps. As a result, the feasible solution set accounts for various constraints due to expected impacts. In a multi-contact situation of under-actuated legged robots subject to multiple impacts, we also enforce standing stability margins. By design, our controller does not require precise knowledge of impact location and timing. We assessed our formalism with the humanoid robot HRP-4, generating maximum contact velocities, neither breaking established contacts nor damaging the hardware.

翻译：刚性机器人在执行有意识碰撞时面临挑战，因为速度与力矩的突变可能导致严重硬件故障。在缺乏专用硬件和控制器的情况下，机器人通常以接近零的速度在接触区域附近运行。我们假设预先已知硬件可吸收的冲击能量，并专注于控制器设计。本方法的新颖性体现在两方面：(i) 采用任务空间逆动力学框架，通过无缝集成冲击任务对其进行扩展；(ii) 无需为执行冲击任务的机器人建立含切换机制的独立模型或重置映射。核心思想在于将碰撞后状态预测与感知冲击的不等式约束整合至现有通用全身控制器中。为完成预测，我们推导了浮动基座机器人沿运动链传播的任务空间冲击效应及伴随的关节速度与力矩跳变。由此，可行解集能涵盖预期冲击引发的各类约束。针对受多冲激作用、欠驱动的多接触腿式机器人，我们还施加了站立稳定性裕度约束。通过设计，本控制器无需精确预知碰撞位置与时间。我们采用人形机器人HRP-4进行验证，实现了最大接触速度，且既未破坏已建立接触关系，也未损坏硬件。