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Modeling realistic human joint limits is important for applications involvingphysical human-robot interaction. However, setting appropriate human jointlimits is challenging because it is pose-dependent: the range of joint motionvaries depending on the positions of other bones. The paper introduces a newtechnique to accurately simulate human joint limits in physics simulation. Wepropose to learn an implicit equation to represent the boundary of valid humanjoint configurations from real human data. The function in the implicitequation is represented by a fully connected neural network whose gradients canbe efficiently computed via back-propagation. Using gradients, we canefficiently enforce realistic human joint limits through constraint forces in aphysics engine or as constraints in an optimization problem.

Data-Driven Approach to Simulating Realistic Human Joint Constraints