Many humanoid and multi-legged robots are controlled in positions rather than in torques, which prevents direct control of contact forces, and hampers their ability to create multiple contacts to enhance their balance, such as placing a hand on a wall or a handrail. This letter introduces the SEIKO (Sequential Equilibrium Inverse Kinematic Optimization) pipeline, and proposes a unified formulation that exploits an explicit model of flexibility 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. Our pipeline is validated with experiments on the real, full-scale humanoid robot Talos in various multi-contact scenarios, including pushing tasks, far-reaching tasks, stair climbing, and stepping on sloped surfaces. Code and videos are available at: https://hucebot.github.io/seiko_controller_website/

翻译：许多人形与多足机器人采用位置控制而非力矩控制，这导致无法直接控制接触力，并限制了其通过建立多重接触来增强平衡能力（例如将手扶于墙壁或扶手）的可能性。本文提出SEIKO（序列平衡逆运动学优化）控制框架，并建立了一种统一数学模型，通过显式建模柔性特征来间接控制传统位置控制型机器人的接触力。SEIKO框架通过实时求解两个二次规划问题，实现了笛卡尔指令到全身运动的重新映射及导纳控制。我们在全尺寸人形机器人Talos上进行了多组多接触场景实验验证，包括推压任务、大范围伸展任务、楼梯攀爬及斜坡踏步等。代码与实验视频详见：https://hucebot.github.io/seiko_controller_website/