[논문]Body Shape-Guided Motion Retargeting to Reduce Effort on Human to Humanoid Landmark Placements

Lim, Chan-Soon; Kwon, Dong-Soo

doi:10.1109/access.2021.3065140

[해외논문] Body Shape-Guided Motion Retargeting to Reduce Effort on Human to Humanoid Landmark Placements 원문보기

IEEE access : practical research, open solutions, v.9, 2021년, pp.40996 - 41009

Lim, Chan-Soon (Korea Advanced Institute of Science and Technology (KAIST), Telerobotics and Control Laboratory, Daejeon, South Korea) , Kwon, Dong-Soo (Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea)

Abstract ▼ AI-Helper

Motion retargeting reduces the animator’s efforts to create robot motion by adapting human motion. However, it still requires a number of manual landmark placements to achieve satisfactory whole-body retargeted motion. Therefore, to reduce efforts on placing landmarks for corresponding minor body parts, this paper proposes a whole-body motion retargeting method for a general humanoid robot that considers the body shape similarity in addition to traditional landmark-based similarity. An additional strategy that matches the volumetric distribution of the body shape between a human and a robot is presented as guidance to handle redundancy from fewer landmarks and to force consistent outcomes from ambiguous landmark placements. The kinematically constrained Gaussian mixture model, originally used as a volumetric model-based human tracking method, is adapted and modified to manage both the shape and the landmarks in the proposed method. The shape and landmark similarity metrics are respectively introduced, and the overall similarity metric is defined by composing the sum of both metrics with weighting coefficients to control the balance between two policies by animators. Then the expectation-maximization based optimization is utilized to calculate target robot angles with human demonstration frame by frame. Experimental results validate the effectiveness of body shape matching, controllability through weighting coefficients, noise robustness on self-retargeting, and generality on applying different humanoid robots.

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