Many humanoid and multi-legged robots are controlled in positions rather than in torques, preventing direct control of contact forces, and hampering their ability to create multiple contacts to enhance their balance, such as placing a hand on a wall or a handrail. This paper introduces the SEIKO (Sequential Equilibrium Inverse Kinematic Optimization) pipeline, drawing inspiration from flexibility models used in serial elastic actuators to indirectly control contact forces on traditional position-controlled robots. SEIKO formulates whole-body retargeting from Cartesian commands and admittance control using two quadratic programs solved in real time. We validated our pipeline with experiments on the real, full-scale humanoid robot Talos in various multicontact scenarios, including pushing tasks, far-reaching tasks, stair climbing, and stepping on sloped surfaces. This work opens the possibility of stable, contact-rich behaviors while getting around many of the challenges of torque-controlled robots. Code and videos are available at https://hucebot.github.io/seiko_controller_website/ .

翻译：许多仿人机器人和多足机器人采用位置控制而非力矩控制，这限制了对接触力的直接调控，并削弱了其通过建立多接触点（如手扶墙壁或扶手）来增强平衡的能力。本文提出SEIKO（序列平衡逆运动学优化）框架，借鉴串联弹性执行器的柔性模型，间接实现传统位置控制型机器人的接触力控制。SEIKO通过实时求解两个二次规划问题，完成了笛卡尔指令的全身重定向与导纳控制。我们在真实全尺寸仿人机器人Talos上进行了多接触场景实验验证，包括推拉任务、远距离伸展任务、爬楼梯及斜面行走。这项工作在规避力矩控制机器人诸多挑战的同时，为稳定、多接触行为开辟了新可能。代码与视频详见https://hucebot.github.io/seiko_controller_website/。