Neural Inverse Kinematics

Raphael Bensadoun*, Shir Gur*, Nitsan Blau, Tom Shenkar, Lior Wolf

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


Inverse kinematic (IK) methods recover the parameters of the joints, given the desired position of selected elements in the kinematic chain. While the problem is well-defined and low-dimensional, it has to be solved rapidly, accounting for multiple possible solutions. In this work, we propose a neural IK method that employs the hierarchical structure of the problem to sequentially sample valid joint angles conditioned on the desired position and on the preceding joints along the chain. In our solution, a hypernetwork f recovers the parameters of multiple primary networks g1, g2,..., gN, where N is the number of joints, such that each gi outputs a distribution of possible joint angles, and is conditioned on the sampled values obtained from the previous primary networks gj, j < i. The hypernetwork can be trained on readily available pairs of matching joint angles and positions, without observing multiple solutions. At test time, a high-variance joint distribution is presented, by sampling sequentially from the primary networks. We demonstrate the advantage of the proposed method both in comparison to other IK methods for isolated instances of IK and with regard to following the path of the end effector in Cartesian space.

Original languageEnglish
Pages (from-to)1787-1797
Number of pages11
JournalProceedings of Machine Learning Research
StatePublished - 2022
Externally publishedYes
Event39th International Conference on Machine Learning, ICML 2022 - Baltimore, United States
Duration: 17 Jul 202223 Jul 2022


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