Fig. 1. Two paths (green and blue arrows) to the top hold in a bouldering problem, as discovered by our method. Compared to previous work, we are not limited to only moving one limb at a time or having the climber's hands and feet on predefined climbing holds; limbs can also hang free for balance, or use the wall for friction. The dashed rectangles highlight the one-to-many mapping from stances (assignments of holds to limbs) to climber states. This paper addresses the problem of offline path and movement planning for wall climbing humanoid agents. We focus on simulating bouldering, i.e. climbing short routes with diverse moves, although we also demonstrate our system on a longer wall. Our approach combines a graph-based highlevel path planner with low-level sampling-based optimization of climbing moves. Although the planning problem is complex, our system produces plausible solutions to bouldering problems (short climbing routes) in less than a minute. We further utilize a k-shortest paths approach, which enables the system to discover alternative paths-in climbing, alternative strategies often exist, and what might be optimal for one climber could be impossible for others due to individual differences in strength, flexibility, and reach. We envision our system could be used, e.g. in learning a climbing strategy, or as a test and evaluation tool for climbing route designers. To the best of our knowledge, this is the first paper to solve and simulate rich humanoid wall climbing, where more than one limb can move at the same time, and limbs can also hang free for balance or use wall friction in addition to predefined holds.

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A common problem of mid-air interaction is excessive arm fatigue, known as the "Gorilla arm" effect. To predict and prevent such problems at a low cost, we investigate user testing of mid-air interaction without real users, utilizing biomechanically simulated AI agents trained using deep Reinforcement Learning (RL). We implement this in a pointing task and four experimental conditions, demonstrating that the simulated fatigue data matches human fatigue data. We also compare two effort models: 1) instantaneous joint torques commonly used in computer animation and robotics, and 2) the recent Three Compartment Controller (3CC-r) model from biomechanical literature. 3CC-r yields movements that are both more efficient and relaxed, whereas with instantaneous joint torques, the RL agent can easily generate movements that are quickly tiring or only reach the targets slowly and inaccurately. Our work demonstrates that deep RL combined with the 3CC-r provides a viable tool for predicting both interaction movements and user experience in silico, without users.

Background: Urinary tract infection (UTI) is one of the most common bacterial diseases in outpatients and inpatients worldwide. Treatment of UTI has become challenging due to the emergence of pathogens with increasing resistance to antimicrobial agents.