Neural Inverse Kinematic

Raphael Bensadoun, Shir Gur, Nitsan Blau, Lior Wolf
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1787-1797, 2022.

Abstract

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 {$g_1,g_2,…,g_N$, where $N$ is the number of joints}, such that each $g_i$ outputs a distribution of possible joint angles, and is conditioned on the sampled values obtained from the previous primary networks $g_j, j

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-bensadoun22a, title = {Neural Inverse Kinematic}, author = {Bensadoun, Raphael and Gur, Shir and Blau, Nitsan and Wolf, Lior}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {1787--1797}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/bensadoun22a/bensadoun22a.pdf}, url = {https://proceedings.mlr.press/v162/bensadoun22a.html}, abstract = {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 {$g_1,g_2,…,g_N$, where $N$ is the number of joints}, such that each $g_i$ outputs a distribution of possible joint angles, and is conditioned on the sampled values obtained from the previous primary networks $g_j, j
Endnote
%0 Conference Paper %T Neural Inverse Kinematic %A Raphael Bensadoun %A Shir Gur %A Nitsan Blau %A Lior Wolf %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-bensadoun22a %I PMLR %P 1787--1797 %U https://proceedings.mlr.press/v162/bensadoun22a.html %V 162 %X 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 {$g_1,g_2,…,g_N$, where $N$ is the number of joints}, such that each $g_i$ outputs a distribution of possible joint angles, and is conditioned on the sampled values obtained from the previous primary networks $g_j, j
APA
Bensadoun, R., Gur, S., Blau, N. & Wolf, L.. (2022). Neural Inverse Kinematic. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:1787-1797 Available from https://proceedings.mlr.press/v162/bensadoun22a.html.

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