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Abstract Research in quadrupedal robotics is transitioning to studies into loco-manipulation, featuring fully articulated robotic arms mounted atop these robots. Integrating such arms enhances the practical utility of legged robots, paving the way for expanded applications like industrial inspection and search and rescue. Existing literature commonly employs a six-degree-of-freedom (six-DoF) arm directly mounted to the robot, which inherently adds significant weight and reduces the available payload for manipulation tasks. Our study explores an optimized combination of arm configuration and control framework by strategically reducing the DoFs and leveraging the quadruped robot’s inherent agile mobility. We demonstrate that by minimizing the DoFs to just one, a range of canonical loco-manipulation tasks can still be accomplished. Some tasks even show improved performance with fewer robotic arm DoFs due to the higher torque motor used in the design, allowing more of the robot’s payload to be used for manipulation. We designed our optimized one-DoF robotic arm and the control framework and tested it on top of a Unitree Aliengo. Our design outperforms conventional six-DoF counterparts in lifting capacity, achieving an impressive 8 kg payload compared to the 2 kg maximum payload of industry-standard six-DoF robotic arms on the same quadruped platform. 

A generalizable machine learning technique (VBO) for efficient exploration of MOF design space was developed and demonstrated by optimizing MOFs for NH3 storage. 

Experimental design techniques such as active search and Bayesian optimization are widely used in the natural sciences for data collection and discovery. However, existing techniques tend to favor exploitation over exploration of the search space, which causes them to get stuck in local optima. This collapse problem prevents experimental design algorithms from yielding diverse high-quality data. In this paper, we extend the Vendi scores—a family of interpretable similarity-based diversity metrics—to account for quality. We then leverage these quality-weighted Vendi scores to tackle experimental design problems across various applications, including drug discovery, materials discovery, and reinforcement learning. We found that quality-weighted Vendi scores allow us to construct policies for experimental design that flexibly balance quality and diversity, and ultimately assemble rich and diverse sets of high-performing data points. Our algorithms led to a 70%–170% increase in the number of effective discoveries compared to baselines. 

Stability prediction is accelerated by treating the convex hull as a probabilistic object, allowing for an efficient active learning process that minimizes the number of thermodynamic calculations necessary to define the convex hull. 

Abstract Applications of machine learning (ML) in atmospheric science have been rapidly growing. To facilitate the development of ML models for tropical cyclone (TC) research, this binary dataset contains a specific customization of the National Center for Environmental Prediction (NCEP)/final analysis (FNL) data, in which key environmental conditions relevant to TC formation are extracted for a range of lead times (0–72 hours) during 1999–2023. The dataset is designed as multi-channel images centered on TC formation locations, with a positive and negative directory structure that can be readily read from any ML applications or common data interface. With its standard structure, this dataset provides users with a unique opportunity to conduct ML application research on TC formation as well as related predictability at different forecast lead times.