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We study the task of learning state representations from potentially high-dimensional observations, with the goal of controlling an unknown partially observable system. We pursue a direct latent model learning approach, where a dynamic model in some latent state space is learned by predicting quantities directly related to planning (e.g., costs) without reconstructing the observations. In particular, we focus on an intuitive cost-driven state representation learning method for solving Linear Quadratic Gaussian (LQG) control, one of the most fundamental partially observable control problems. As our main results, we establish finite-sample guarantees of finding a near-optimal state representation function and a near-optimal controller using the directly learned latent model. To the best of our knowledge, despite various empirical successes, prior to this work it was unclear if such a cost-driven latent model learner enjoys finite-sample guarantees. Our work underscores the value of predicting multi-step costs, an idea that is key to our theory, and notably also an idea that is known to be empirically valuable for learning state representations. 

Tele-operated social robots (telerobots) offer an innovative means of allowing children who are medically restricted to their homes (MRH) to return to their local schools and physical communities. Most commercially available telerobots have three foundational features that facilitate child–robot interaction: remote mobility, synchronous two-way vision capabilities, and synchronous two-way audio capabilities. We conducted a comparative analysis between the Toyota Human Support Robot (HSR) and commercially available telerobots, focusing on these foundational features. Children who used these robots and these features on a daily basis to attend school were asked to pilot the HSR in a simulated classroom for learning activities. As the HSR has three additional features that are not available on commercial telerobots: (1) pan-tilt camera, (2) mapping and autonomous navigation, and (3) robot arm and gripper for children to “reach” into remote environments, participants were also asked to evaluate the use of these features for learning experiences. To expand on earlier work on the use of telerobots by remote children, this study provides novel empirical findings on (1) the capabilities of the Toyota HSR for robot-mediated learning similar to commercially available telerobots and (2) the efficacy of novel HSR features (i.e., pan-tilt camera, autonomous navigation, robot arm/hand hardware) for future learning experiences. We found that among our participants, autonomous navigation and arm/gripper hardware were rated as highly valuable for social and learning activities. 

A series of twelve two-coordinate coinage metal, Cu, Ag and Au, complexes with carbene-metal-amide structures were prepared. The complexes all display thermal assisted delayed fluorescence (TADF) emission at room temperature from interligand charge transfer (ICT) excited state with short lifetimes (less than 2 μs) and photoluminescent quantum yields that reach near unity. Owing to the involvement of the substituents in the emissive transitions and different metal ion volume, the natural transition orbital (NTO) overlap of the emissive state can be adjusted in a wide range from 0.21 to 0.41. Investigations on the relationship between the NTO overlap of the emissive state and key TADF photophysical properties demonstrated that both singlet–triplet energy gap and radiative decay rate of S 1 state increase along with the NTO overlap exponentially. Consequently, the overall TADF radiative decay rate leads to a maximum when plotted against the NTO overlap, giving the ideal zone from 0.25 to 0.30 for high TADF radiative decay rate in this class of two-coordinate coinage metal complex luminophores.