This letter presents a data-driven motion retargeting method with safety considerations. In particular, we focus on handling self-collisions while transferring poses between different domains. To this end, we first propose leveraged Wasserstein auto-encoders (LWAE) which leverage both positive and negative data where negative data consist of self-collided poses. Then, we extend this idea to multiple domains to have a shared latent space to perform motion retargeting. We also present an effective self-collision handling method based on solving inverse kinematics with augmented targets that is used to collect collision-free poses. The proposed method is extensively evaluated in a diverse set of motions from human subjects and an animation character where we show that incorporating negative data dramatically reduces self-collisions while preserving the quality of the original motion.
Bibliographical notePublisher Copyright:
© 2016 IEEE.
- Deep learning in robotics and automation
- collision avoidance
- motion and path planning
ASJC Scopus subject areas
- Control and Systems Engineering
- Biomedical Engineering
- Human-Computer Interaction
- Mechanical Engineering
- Computer Vision and Pattern Recognition
- Computer Science Applications
- Control and Optimization
- Artificial Intelligence