An electromagnetic haptic feedback system with receding horizon optimal control and an approximate model guides drawing at user pace, achieving 2.8mm pen position dispersion and improved accuracy on complex shapes.
In Proceedings of the 17th annual ACM symposium on User interface software and technology
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SCAL derives an upper bound on target-domain imitation loss using source loss plus state-conditional latent KL divergence and aligns distributions via a discriminator-based adversarial estimator.
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Dynamic Drawing Guidance via Electromagnetic Haptic Feedback
An electromagnetic haptic feedback system with receding horizon optimal control and an approximate model guides drawing at user pace, achieving 2.8mm pen position dispersion and improved accuracy on complex shapes.
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State-Conditional Adversarial Learning: An Off-Policy Visual Domain Transfer Method for End-to-End Imitation Learning
SCAL derives an upper bound on target-domain imitation loss using source loss plus state-conditional latent KL divergence and aligns distributions via a discriminator-based adversarial estimator.