One of the core goals of crisis counseling services is to support emotional de-escalation of the individual in crisis, by reducing intense negative emotional affect and emotional dysregulation.The science of crisis intervention has been impeded, however, by a lack of quantitative approaches that allow for detailed analysis of emotion in crisis conversations.In order to measure de-escalation at scale (millions of textbased conversations), lightweight models are needed that can assign not just binary sentiment predictions but quantitative scores to capture graded change in emotional valence.Accordingly, we developed a transformer-based emotional valence scoring model fit for crisis conversations, BERT-EV, that assigns numerical emotional valence scores to rate the intensity of expressed negative versus positive emotion.This transformer-based model can run on modest hardware configurations, allowing it to scale affordably and efficiently to a massive corpus of crisis conversations.We evaluated model performance on a corpus of hand-scored social media messages, and found that BERT-EV outperforms existing dictionary-based standard tools in the field, as well as other transformerbased implementations and an LLM in accurately matching scores from human annotators.Finally, we show that trends in these emotional valence scores can be used to assess emotional de-escalation during crisis conversations, with sufficient turn-by-turn granularity to help identify helpful vs. detrimental crisis counselor statements.

Particle filtering is a common technique for six degree of freedom (6D) pose estimation due to its ability to tractably represent belief over object pose. However, the particle filter is prone to particle deprivation due to the high-dimensional nature of 6D pose. When particle deprivation occurs, it can cause mode collapse of the underlying belief distri-bution during importance sampling. If the region surrounding the true state suffers from mode collapse, recovering its belief is challenging since the area is no longer represented in the probability mass formed by the particles. Previous methods mitigate this problem by randomizing and resetting particles in the belief distribution, but determining the frequency of reinvigoration has relied on hand-tuning abstract heuristics. In this paper, we estimate the necessary reinvigoration rate at each time step by introducing a Counter-Hypothetical likelihood function, which is used alongside the standard likelihood. Inspired by the notions of plausibility and implausibility from Evidential Reasoning, the addition of our Counter-Hypothetical likelihood function assigns a level of doubt to each particle. The competing cumulative values of confidence and doubt across the particle set are used to estimate the level of failure within the filter, in order to determine the portion of particles to be reinvigorated. We demonstrate the effectiveness of our method on the rigid body object 6D pose tracking task.

Advancements in autonomous agents have led to an increasingly ubiquitous presence of robots in human environments where social and physical interaction is expected. Such environments are often composed of heterogeneous agents with disparate action capabilities, intentions, and motivations. Intra- and inter-agent dissimilarity often prevents enacting effective behavioral skills (e.g., collaboration, communication, coordination) towards dynamic task allocation objectives. We propose a Bayesian probabilistic inference approach, MultiRobot Belief Propagation with Identity constraints (MRPBI), for 1) decentralized task allocation in multi-agent systems and 2) modeling task affinity using personal identities. MRPBI leverages competing costs of individual and group capabilities that result in less error-prone convergence to steady state, scalability without loss of accuracy, and sensitivity to environmental dynamics. An implementation of MRBP-I as a distributed algorithm that weighs factors of both individual and cooperative perception in an energy-minimizing task allocation scheme is presented.

Stereo cameras are widely used for sensing and navigation of underwater robotic systems. They can provide high resolution color views of a scene; the constrained camera geometry enables metrically accurate depth estimation; they are also relatively cost-effective. Traditional stereo vision algorithms rely on feature detection and matching to enable triangulation of points for estimating disparity. However, for underwater applications, the effects of underwater light propagation lead to image degradation, reducing image quality and contrast. This makes it especially challenging to detect and match features, especially from varying viewpoints. Recently, deep learning has shown success in end-to-end learning of dense disparity maps from stereo images. Still, many state-of-the-art methods are supervised and require ground truth depth or disparity, which is challenging to gather in subsea environments. Simultaneously, deep learning has also been applied to the problem of underwater image restoration. Again, it is difficult or impossible to gather real ground truth data for this problem. In this work, we present an unsupervised deep neural network (DNN) that takes input raw color underwater stereo imagery and outputs dense depth maps and color corrected imagery of underwater scenes. We leverage a model of the process of underwater image formation, image processing techniques, as well as the geometric constraints inherent to the stereo vision problem to develop a modular network that outperforms existing methods.