The number of joints and muscles in a human arm is more than what is required for reaching to a desired point in 3D space. Although previous studies have emphasized how such redundancy and the associated flexibility may play an important role in path planning, control of noise, and optimization of motion, whether and how redundancy might promote motor learning has not been investigated. In this work, we quantify redundancy space and investigate its significance and effect on motor learning. We propose that a larger redundancy space leads to faster learning across subjects. We observed this pattern in subjects learning novel kinematics (visuomotor adaptation) and dynamics (force-field adaptation). Interestingly, we also observed differences in the redundancy space between the dominant hand and nondominant hand that explained differences in the learning of dynamics. Taken together, these results provide support for the hypothesis that redundancy aids in motor learning and that the redundant component of motor variability is not noise.supervised learning | minimum-intervention principle | reaching | motor noise | motor control R edundancy is a ubiquitous property that renders biological systems robust to disruptions. Goal-directed movements also display redundancy because a given movement, such as touching one's nose, can be made in many different ways with different combinations of joint angles. Although redundancy generates flexibility, it also poses a fundamental problem for the motor system because a large component of motor variability is attributed to muscle noise (1). Thus, if muscles operated independently, the total noise (reflected in the trajectory) would be a summation of the noise due to the component parts. However, if the covariation of muscles or joints occurs such that the effect of individual muscle or joint variability is mitigated, it is possible to maintain acceptable levels of variability while maintaining a reasonable degree of redundancy that allows flexibility in behavior. Consistent with this view, it has been observed in a wide range of tasks (2-7) that variability is not eliminated but optimized (8-11) to accumulate in a task-relevant dimensions using a minimum-intervention principle (12). Such variability, that is a consequence of redundancy, can be quantified as an uncontrolled manifold (13-15) in which task-independent variability is constrained to a redundant subspace (or "uncontrolled manifold").Although minimizing variability is expected to improve taskrelated performance, recent evidence suggests that motor variability paradoxically helps in motor learning (16)(17)(18)(19)(20). Such findings are supported by ideas in reinforcement learning (21,22), which suggest that baseline variability increases exploration, which in turn facilitates learning. Interestingly, motor variability has also been shown to help learning during supervised error-based learning tasks (16), suggesting a more general role of variability in motor learning. In this study, we tested whether variability arising ...