When legged robots impact their environment executing dynamic motions, they undergo large changes in their velocities in a short amount of time. Measuring and applying feedback to these velocities is challenging, further complicated by uncertainty in the impact model and impact timing. This work proposes a general framework for adapting feedback control during impact by projecting the control objectives to a subspace that is invariant to the impact event. The resultant controller is robust to uncertainties in the impact event while maintaining maximum control authority over the impact-invariant subspace. We demonstrate the improved performance of the projection over other commonly used heuristics on a walking controller for a planar five-link-biped. The projection is also applied to jumping, box jumping, and running controllers for the compliant 3D bipedal robot, Cassie. The modification is easily applied to these various controllers and is a critical component to deploying on the physical robot. Code and video of the experiments are available at https://impact-invariant-control.github.io/.

翻译：当足式机器人在执行动态运动时与环境发生冲击，其速度会在短时间内发生剧烈变化。对这些速度进行测量和反馈控制具有挑战性，而冲击模型和冲击时机的不确定性进一步加剧了难度。本研究提出一个通用框架，通过将控制目标投影到对冲击事件保持不变的子空间，从而在冲击期间调整反馈控制。所得控制器对冲击事件的不确定性具有鲁棒性，同时在冲击不变子空间上保持最大的控制权限。我们通过一个平面五连杆双足机器人的步行控制器，展示了该投影方法相较于其他常用启发式方法的性能提升。该投影方法同样适用于Cassie柔性三维双足机器人的跳跃、箱式跳跃和奔跑控制器。此修改可轻松应用于各类控制器，是部署于物理机器人的关键组成部分。实验代码和视频可在 https://impact-invariant-control.github.io/ 获取。