This paper proposes a novel robust adaptive model predictive controller for on-orbit dislodging. We consider the scenario where a servicer, equipped with a robot arm, must dislodge a client, a time-varying system composed of an underpowered jammed solar panel with a hybrid hinge system on a space station. Our approach leverages online set-membership identification to reduce the uncertainty to provide robust safety guarantees during dislodging despite bounded disturbances while balancing exploration and exploitation effectively in the parameter space. The feasibility of the developed robust adaptive MPC method is also examined through dislodging simulations and hardware experiments in zero-gravity and gravity environments, respectively. In addition, the advantages of our method are shown through comparison experiments with several state-of-the-art control schemes for both accuracy of parameter estimation and control performance.

This study develops an AI-based implementation of autonomous On-Orbit Servicing (OOS) mission to assist with spacecraft collision avoidance maneuvers (CAMs). We propose an autonomous `servicer' trained with Reinforcement Learning (RL) to autonomously detect potential collisions between a target satellite and space debris, rendezvous and dock with endangered satellites, and execute optimal CAM. The RL model integrates collision risk estimates, satellite specifications, and debris data to generate an optimal maneuver matrix for OOS rendezvous and collision prevention. We employ the Cross-Entropy algorithm to find optimal decision policies efficiently. Initial results demonstrate the feasibility of autonomous robotic OOS for collision avoidance services, focusing on one servicer spacecraft to one endangered satellite scenario. However, merging spacecraft rendezvous and optimal CAM presents significant complexities. We discuss design challenges and critical parameters for the successful implementation of the framework presented through a case study.

Mortgages account for the largest portion of household debt in the United States, totaling around \$12 trillion nationwide. In times of financial hardship, alleviating mortgage burdens is essential for supporting affected households. The mortgage servicing industry plays a vital role in offering this assistance, yet there has been limited research modelling the complex relationship between households and servicers. To bridge this gap, we developed an agent-based model that explores household behavior and the effectiveness of relief measures during financial distress. Our model represents households as adaptive learning agents with realistic financial attributes. These households experience exogenous income shocks, which may influence their ability to make mortgage payments. Mortgage servicers provide relief options to these households, who then choose the most suitable relief based on their unique financial circumstances and individual preferences. We analyze the impact of various external shocks and the success of different mortgage relief strategies on specific borrower subgroups. Through this analysis, we show that our model can not only replicate real-world mortgage studies but also act as a tool for conducting a broad range of what-if scenario analyses. Our approach offers fine-grained insights that can inform the development of more effective and inclusive mortgage relief solutions.

This paper presents a neural network-based Unscented Kalman Filter (UKF) to estimate and track the pose (i.e., position and orientation) of a known, noncooperative, tumbling target spacecraft in a close-proximity rendezvous scenario. The UKF estimates the target's orbit and attitude relative to the servicer based on the pose information provided by a multi-task Convolutional Neural Network (CNN) from incoming monocular images of the target. In order to enable reliable tracking, the process noise covariance matrix of the UKF is tuned online using adaptive state noise compensation which leverages a newly developed closed-form process noise model for relative attitude dynamics. This paper also introduces the Satellite Hardware-In-the-loop Rendezvous Trajectories (SHIRT) dataset to enable comprehensive analyses of the performance and robustness of the proposed pipeline. SHIRT comprises the labeled images of two representative rendezvous trajectories in low Earth orbit created using both a graphics renderer and a robotic testbed. Specifically, the CNN is solely trained on synthetic data, whereas functionality and performance of the complete navigation pipeline are evaluated on real images from the robotic testbed. The proposed UKF is evaluated on SHIRT and is shown to have sub-decimeter-level position and degree-level orientation errors at steady-state.

Speaking to DS News, Gilmore discussed the current state of servicing, COVID-19's impact, and how artificial intelligence and machine learning can be …

A new study or survey is released almost daily that suggests that artificial intelligence (AI) and machine learning (ML) will revolutionize our lives. This past summer, the Treasury Department released a report in which the agency recommended facilitating the development of AI due to the potential it holds for financial services companies and the overall economy. The agency also found that AI was one of the three biggest areas of investment for financial services companies last year. However, it's not just the Treasury Department that is backing AI and machine learning. The Federal Reserve has recognized the two concepts, as has the Financial Industry Regulatory Authority (FINRA), which noted that AI could help banks prevent money laundering and improve data management and customer service.

Sign up here for our New Managers publication and get each issue by email as Opalesque publishes them. A new series on technology providers that assist asset allocators. Blue Lion Research, a City of London Fintech company, is bringing in artificial intelligence (AI) to provide deeper analysis for investors and managers. For investors, the firm assesses operational criteria, clauses and features of legal documents, and provides comparison with peers and response to market conditions. And for managers, they evaluate differentiation and provide a better understanding of their fund universe. Their new product launched in August 2016 after two years of research and development.

What is the difference between all the companies working on machine learning? There are three different types of Machine Learning and Artificial Intelligence companies: The Superrich, the Servicers, and the Innovators. All three of these type of companies can be massively successful and each one has a distinct flavor. The Superrich are companies like Google, Facebook, Baidu, Tencent, Amazon, Microsoft, and others. There are very few of these companies in the world but they have a massive advantage over everyone else in the machine learning space because they have access to vast amounts of clean, structured data.