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姿控制而非力矩控制，这限制了接触力的直接调节能力，也阻碍了通过建立多接触点（如将手置于墙壁或扶手）来增强平衡稳定性的方法。本文借鉴串联弹性致动器中的柔性模型，提出SEIKO（序列平衡逆运动学优化）流水线，用于间接控制传统位姿控制机器人的接触力。SEIKO通过实时求解两个二次规划问题，实现了笛卡尔指令的全身重定向与导纳控制。我们采用真实全尺寸仿人机器人Talos，在推拉任务、远距离抓取、爬楼梯及斜面行走等多接触场景中验证了该流水线的有效性。本工作为规避力矩控制机器人诸多挑战、实现稳定且具有丰富接触行为的控制策略开辟了新的可能性。代码与演示视频详见https://hucebot.github.io/seiko_controller_website/