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Conference Papers Year : 2024

Unsupervised Motion Retargeting for Human-Robot Imitation


This early-stage research work aims to improve online human-robot imitation by translating sequences of joint positions from the domain of human motions to a domain of motions achievable by a given robot, thus constrained by its embodiment. Leveraging the generalization capabilities of deep learning methods, we address this problem by proposing an encoder-decoder neural network model performing domain-to-domain translation. In order to train such a model, one could use pairs of associated robot and human motions. Though, such paired data is extremely rare in practice, and tedious to collect. Therefore, we turn towards deep learning methods for unpaired domain-to-domain translation, that we adapt in order to perform human-robot imitation.
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Dates and versions

hal-04401885 , version 1 (18-01-2024)





Louis Annabi, Ziqi Ma, Sao Mai Nguyen. Unsupervised Motion Retargeting for Human-Robot Imitation. HRI 2024 - Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, Mar 2024, Boulder (CO), United States. ⟨10.1145/3568294.3580153⟩. ⟨hal-04401885⟩
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