Abstract

Controlling humanoid robot locomotion is in general challenging, while biological systems demonstrate adaptive and robust movement with minimal control effort. In that sense, human kinematics hold potential for locomotion controller design. Among various human movements, squatting serves as a fundamental behavior that integrates both stance and balance subfunctions of locomotion. This work investigates how observed human kinematics can be leveraged to control squatting motions in a humanoid robot.We propose a bioinspired control scheme in which recorded human joint angles parameterize a reference trajectory that is tracked by simple low-level PD controllers. To isolate the effect of human kinematic structure, we deliberately avoid learning-based or optimization-heavy controllers and examine how far simple reference generation can reproduce human-like squatting. This scheme is implemented on a detailed simulation model of our humanoid robot. We explore the parameter space of joint gains to evaluate three key performance metrics: Stability, Efficiency, and Similarity.Simulation results show that the kinematics-based reference generation, combined with simple closed-loop control tracking, is effective in producing human-like squatting behaviors. By tuning the gains, trade-offs among stability, efficiency, and similarity can be achieved to obtain balanced performance. This work demonstrates the feasibility of transferring human squatting principles to robotic systems with simple, yet efficient control schemes and systematically evaluates the resulting behaviors within a structured performance framework.

Link: https://ieeexplore.ieee.org/document/11625408