Daqing Yi

TL;DR: This paper shows how viewing humanrobot language through the lens of shared autonomy explains the efficiency versus cognitive load trade-offs humans make when deciding how cooperative and explicit to make their instructions.

TL;DR: This work introduces an algorithmic framework to model qualitative information as quantitative constraints on and between states as well as Sequentially Constrained Monte Carlo sampling, which is able to generate samples that satisfy arbitrarily complex, non-smooth and discontinuous constraints.

TL;DR: In this article, the authors examine the problem of desk organization: learning how humans spatially position different objects on a planar surface according to organizational preference, by examining how humans position objects given multiple features received from vision and haptic modalities.

TL;DR: For a wide range of scenarios that using the sampler can accelerate the convergence rate to high-quality solutions in high-dimensional problems, Monte Carlo sampling methods are applied, used very effectively in the Machine Learning and Optimization communities, to solve the problem.