# Self-Supervised Motion Retargeting with Safety Guarantee

arXiv: https://arxiv.org/abs/2103.06447 · HTML: https://mlsanity.com/paper/2103.06447/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 11 of 11)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CURL: Contrastive Unsupervised Representations for Reinforcement Learning](https://mlsanity.com/paper/2004.04136/) (2020)
- [Time-Contrastive Networks: Self-Supervised Learning from Video](https://mlsanity.com/paper/1704.06888/) (2017)
- [Wasserstein Auto-Encoders](https://mlsanity.com/paper/1711.01558/) (2017)
- [Self-supervised 6D Object Pose Estimation for Robot Manipulation](https://mlsanity.com/paper/1909.10159/) (2019)
- [Neural Kinematic Networks for Unsupervised Motion Retargetting](https://mlsanity.com/paper/1804.05653/) (2018)
- [FrankMocap: Fast Monocular 3D Hand and Body Motion Capture by Regression and Integration](https://mlsanity.com/paper/2008.08324/) (2020)
- [Approximate Nearest Neighbor Search in High Dimensions](https://mlsanity.com/paper/1806.09823/) (2018)
- [Learning to Fly by Crashing](https://mlsanity.com/paper/1704.05588/) (2017)
- [Learning Actionable Representations from Visual Observations](https://mlsanity.com/paper/1808.00928/) (2018)

## Built on by (most cited first, 6 of 6)

- [ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space](https://mlsanity.com/paper/2309.05310/) (2023)
- [Robot Interaction Behavior Generation based on Social Motion Forecasting for Human-Robot Interaction](https://mlsanity.com/paper/2402.04768/) (2024)
- [Learning Whole-Body Human-Humanoid Interaction from Human-Human Demonstrations](https://mlsanity.com/paper/2601.09518/) (2026)
- [GBC: Generalized Behavior-Cloning Framework for Whole-Body Humanoid Imitation](https://mlsanity.com/paper/2508.09960/) (2025)
- [Redefining Data Pairing for Motion Retargeting Leveraging a Human Body Prior](https://mlsanity.com/paper/2409.13208/) (2024)
- [HIGenNTO: Scalable Humanoid Interaction Generation via Noise-Space Trajectory Optimization](https://mlsanity.com/paper/2609.22611/) (2026)

## Abstract (arXiv)

In this paper, we present self-supervised shared latent embedding (S3LE), a data-driven motion retargeting method that enables the generation of natural motions in humanoid robots from motion capture data or RGB videos. While it requires paired data consisting of human poses and their corresponding robot configurations, it significantly alleviates the necessity of time-consuming data-collection via novel paired data generating processes. Our self-supervised learning procedure consists of two steps: automatically generating paired data to bootstrap the motion retargeting, and learning a projection-invariant mapping to handle the different expressivity of humans and humanoid robots. Furthermore, our method guarantees that the generated robot pose is collision-free and satisfies position limits by utilizing nonparametric regression in the shared latent space. We demonstrate that our method can generate expressive robotic motions from both the CMU motion capture database and YouTube videos.