David Wadden, Kejian Shi, Jacob Morrison, Alan Li, Aakanksha Naik, Shruti Singh, Nitzan Barzilay, Kyle Lo, Tom Hope, Luca Soldaini, Shannon Zejiang Shen, Doug Downey, Hannaneh Hajishirzi, Arman Cohan. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

6D Object pose estimation is a fundamental component in robotics enabling efficient interaction with the environment. 6D pose estimation is particularly challenging in bin- picking applications, where many objects are low-feature and reflective, and self-occlusion between objects of the same type is common. We propose a novel multi-view approach leveraging known camera transformations from an eye-in-hand setup to combine heatmap and keypoint estimates into a probability density map over 3D space. The result is a robust approach that is scalable in the number of views. It relies on a confidence score composed of keypoint probabilities and point-cloud alignment error, which allows reliable rejection of false positives. We demonstrate an average pose estimation error of approximately 0.5 mm and 2 degrees across a variety of difficult low-feature and reflective objects in the ROBI dataset, while also surpassing the state-of-art correct detection rate, measured using the 10% object diameter threshold on ADD error.

We describe a suite of scalable software methods and frameworks to helps schedule payload operations of a large constellation, with multiple payloads per and across spacecraft, such that the collection of observational data and their downlink, constrained by the constellation constraints (orbital mechanics), resources (e.g., power) and subsystems (e.g., attitude control), results in maximum science value for a selected use case. Constellation topology, spacecraft and ground network characteristics can be imported from design tools or existing constellations and can serve as elements of an operations design tool. Our framework includes a science simulator to inform the scheduler of the predictive value of observations or operational decisions. Autonomous, realtime re-scheduling based on past observations needs improved data assimilation methods within the simulator.

D-SHIELD is a suite of scalable software tools that helps schedule payload operations of a large constellation, with multiple payloads per and across spacecraft, such that the collection of observational data and their downlink, constrained by the constellation constraints (orbital mechanics), resources (e.g., power) and subsystems (e.g., attitude control), results in maximum science value for a selected use case. Constellation topology, spacecraft and ground network characteristics can be imported from design tools or existing constellations and can serve as elements of an operations design tool. D-SHIELD will include a science simulator to inform the scheduler of the predictive value of observations or operational decisions. Autonomous, realtime re-scheduling based on past observations needs improved data assimilation methods within the simulator.

Solar car race competitions offer realistic conditions to test and demonstrate the state-of-the-art technologies in multidisciplinary fields. In such races the solar panels mounted on the car produce the energy required to power the vehicle. A simulator runs during the race determines the optimal race speed based on the predicted availability of solar energy and other parameters as well as road conditions. The accuracy of the forecasts, especially the solar irradiance forecasts, has a significant impact on the race strategy. Here we report on the experience of providing irradiance forecasts for two races run by the University of Michigan Solar Car Team at the Bridgestone World Solar Challenge 2015 in Australia and at the American Solar Challenge 2016 from Ohio to South Dakota. The probabilistic forecasts of hourly solar irradiance generated from machine learning algorithms were deployed to optimally decide on the race strategy. This work showcases an example of real time decision making based on insights derived from machine learning utilizing big geospatial data — weather models and measurement data from weather station networks.

Continuous analytics systems that enable query processing over steams of data have emerged as key solutions for dealing with massive data volumes and demands for low latency. These systems have been heavily influenced by an assumption that data streams can be viewed as sequences of data that arrived more or less in order. The reality, however, is that streams are not often so well behaved and disruptions of various sorts are endemic. We argue, therefore, that stream processing needs a fundamental rethink and advocate a unified approach toward continuous analytics over discontinuous streaming data. Our approach is based on a simple insight - using techniques inspired by data parallel query processing, queries can be performed over independent sub-streams with arbitrary time ranges in parallel, generating partial results. The consolidation of the partial results over each sub-stream can then be deferred to the time at which the results are actually used on an on-demand basis. In this paper, we describe how the Truviso Continuous Analytics system implements this type of order-independent processing. Not only does the approach provide the first real solution to the problem of processing streaming data that arrives arbitrarily late, it also serves as a critical building block for solutions to a host of hard problems such as parallelism, recovery, transactional consistency, high availability, failover, and replication.