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Kinematic Motion Retargeting via Neural Latent Optimization for Learning Sign Language ...
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Abstract:
Motion retargeting from a human demonstration to a robot is an effective way to reduce the professional requirements and workload of robot programming, but faces the challenges resulting from the differences between humans and robots. Traditional optimization-based methods are time-consuming and rely heavily on good initialization, while recent studies using feedforward neural networks suffer from poor generalization to unseen motions. Moreover, they neglect the topological information in human skeletons and robot structures. In this paper, we propose a novel neural latent optimization approach to address these problems. Latent optimization utilizes a decoder to establish a mapping between the latent space and the robot motion space. Afterward, the retargeting results that satisfy robot constraints can be obtained by searching for the optimal latent vector. Alongside with latent optimization, neural initialization exploits an encoder to provide a better initialization for faster and better convergence of ...
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Keyword:
FOS Computer and information sciences; Robotics cs.RO
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URL: https://dx.doi.org/10.48550/arxiv.2103.08882
https://arxiv.org/abs/2103.08882
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