The human musculoskeletal system’s inherent redundancies allow for infinite potential configurations for any given task. While sometimes seen as a problem for cognitive control systems, motor redundancy also fosters adaptability, learning, and resilience, making it essential for effective motor functioning (Latash, 2012). While many features of human motion and pose production have been identified, it remains unclear how cognitive systems quickly identify and enact motions given the scale of challenges introduced by motor redundancy. This study introduces an inverse kinematics solver, the Forward and Backward Reaching Inverse Kinematics solver (FABRIK) (Aristidou et al., 2016; Lamb et al., 2022). FABRIK uses a novel and lightweight approach to overcoming degree of freedom redundancy in multi-joint systems and may provide insights into human motor control. Initial validations of FABRIK for predicting human motion and pose data, demonstrate strong alignment with recorded data and are comparable to more computationally intensive state-of-the-art methods. We consider the implications of this relatively simple inverse kinematics solver for understanding how cognitive systems might deal with the challenges of motion planning in real time.