Shared autonomy holds promise for improving the usability and accessibility of assistive robotic arms, but current methods often rely on costly expert demonstrations and remain static after pretraining, limiting their ability to handle real-world variations. Even with extensive training data, unforeseen challenges—especially those that fundamentally alter task dynamics, such as unexpected obstacles or spatial constraints—can cause assistive policies to break down, leading to ineffective or unreliable assistance. To address this, we propose ILSA, an Incrementally Learned Shared Autonomy framework that continuously refines its assistive policy through user interactions, adapting to real-world challenges beyond the scope of pre-collected data. At the core of ILSA is a structured fine-tuning mechanism that enables continual improvement with each interaction by effectively integrating limited new interaction data while helping to preserve prior knowledge, aiming for a balance between adaptation and generalization. A user study with 20 participants demonstrates ILSA’s effectiveness, showing faster task completion and improved user experience compared to static alternatives. Code and videos are available at https://ilsa-robo.github.io/.

Robots can perform various types of automated movements in the workspace. In recent years, robot applications have been expanded to a much wider scope, including robot machining, robot assembly, robot 3D printing, robot inspection, etc. Many of these applications require robots to have higher absolute accuracy compared with conventional robot part handling and welding. The capability to assess a robot’s accuracy, and further, improve accuracy becomes important. In this paper, an advanced sensor and an accompanying methodology are developed that enable manufacturers to perform accuracy assessment to improve their robot systems. A smart target (patent pending) is developed at the National Institute of Standards and Technology (NIST). This sensor is integrated with a vision-based measurement instrument to perform high accuracy measurements of six-dimensional (6-D) information (x, y, and z position, roll, pitch, and yaw orientation) for a moving robot arm. The smart target is motorized. It can constantly rotate toward the vision-based measurement instrument to maximize its line-of-sight. A use case is presented to demonstrate the accuracy degradation assessment by using the smart target on a Universal Robot.

Robot calibration and performance will degrade if proper maintenance isn't performed. There have been challenges for manufacturers to optimize the maintenance strategy and minimize unexpected shutdowns. Prognostics and health management (PHM) can be applied to industrial robots through the development of performance metrics, test methods, reference datasets, and supporting tools. A subset of this research involves developing a quick health assessment methodology emphasizing the identification of the positional health (position and orientation accuracy) changes. This methodology enables manufacturers to quickly assess the static/dynamic position and orientation accuracies of their robot systems. In this paper, the National Institute of Standards and Technology's (NIST) effort to develop the measurement science to support this development is presented, including the modeling and algorithm development for the test method, the advanced sensor development to measure 7-D information (time, X, Y, Z, roll, pitch, and yaw), algorithms to analyze the data, and a use case to present the results.