Improved additive manufacturing capabilities are vital for the future development and improvement of ubiquitous robotic systems. These machines can be integrated into existing robotic systems to allow manufacturing and repair of components, as well as fabrication of custom parts for the robots themselves. The fused filament fabrication (FFF) process is one of the most common and well-developed AM processes but suffers from the effects of vibration-induced position error, particularly as the printing speed is raised. This project adapted and expanded a dynamic model of an FFF gantry system to include a passive spring-mass-damper system controller attached to the extruder carriage and tuned using optimal parameters. A case study was conducted to demonstrate the effects and generate recommendations for implementation. This work is also valuable for other mechatronic systems which operate using an open-loop control system and which suffer from vibration, including numerous robotic systems, pick-and-place machines, positioners, and similar.

First-order model counting (FOMC) is the problem of counting the number of models of a sentence in first-order logic. Since lifted inference techniques rely on reductions to variants of FOMC, the design of scalable methods for FOMC has attracted attention from both theoreticians and practitioners over the past decade. Recently, a new approach based on first-order knowledge compilation was proposed. This approach, called Crane, instead of simply providing the final count, generates definitions of (possibly recursive) functions that can be evaluated with different arguments to compute the model count for any domain size. However, this approach is not fully automated, as it requires manual evaluation of the constructed functions. The primary contribution of this work is a fully automated compilation algorithm, called Gantry, which transforms the function definitions into C++ code equipped with arbitrary-precision arithmetic. These additions allow the new FOMC algorithm to scale to domain sizes over 500,000 times larger than the current state of the art, as demonstrated through experimental results.

The resurgence and rapid advancement of Generative Artificial Intelligence (GenAI) in 2023 has catalyzed transformative shifts across numerous industry sectors, including urban transportation and logistics. This study investigates the evaluation of Large Language Models (LLMs), specifically GPT-4 and Phi-3-mini, to enhance transportation planning. The study assesses the performance and spatial comprehension of these models through a transportation-informed evaluation framework that includes general geospatial skills, general transportation domain skills, and real-world transportation problem-solving. Utilizing a mixed-methods approach, the research encompasses an evaluation of the LLMs' general Geographic Information System (GIS) skills, general transportation domain knowledge as well as abilities to support human decision-making in the real-world transportation planning scenarios of congestion pricing. Results indicate that GPT-4 demonstrates superior accuracy and reliability across various GIS and transportation-specific tasks compared to Phi-3-mini, highlighting its potential as a robust tool for transportation planners. Nonetheless, Phi-3-mini exhibits competence in specific analytical scenarios, suggesting its utility in resource-constrained environments. The findings underscore the transformative potential of GenAI technologies in urban transportation planning. Future work could explore the application of newer LLMs and the impact of Retrieval-Augmented Generation (RAG) techniques, on a broader set of real-world transportation planning and operations challenges, to deepen the integration of advanced AI models in transportation management practices.

This article presents the first field deployment of a multi-agent reinforcement-learning (MARL) based variable speed limit (VSL) control system on the I-24 freeway near Nashville, Tennessee. We describe how we train MARL agents in a traffic simulator and directly deploy the simulation-based policy on a 17-mile stretch of Interstate 24 with 67 VSL controllers. We use invalid action masking and several safety guards to ensure the posted speed limits satisfy the real-world constraints from the traffic management center and the Tennessee Department of Transportation. Since the time of launch of the system through April, 2024, the system has made approximately 10,000,000 decisions on 8,000,000 trips. The analysis of the controller shows that the MARL policy takes control for up to 98% of the time without intervention from safety guards. The time-space diagrams of traffic speed and control commands illustrate how the algorithm behaves during rush hour. Finally, we quantify the domain mismatch between the simulation and real-world data and demonstrate the robustness of the MARL policy to this mismatch.

This paper introduces a novel approach that seeks a middle ground for traffic control in multi-lane congestion, where prevailing traffic speeds are too fast, and speed recommendations designed to dampen traffic waves are too slow. Advanced controllers that modify the speed of an automated car for wave-dampening, eco-driving, or other goals, typically are designed with forward collision safety in mind. Our approach goes further, by considering how dangerous it can be for a controller to drive so slowly relative to prevailing traffic that it creates a significant issue for safety and comfort. This paper explores open-road scenarios where large gaps between prevailing speeds and desired speeds can exist, specifically when infrastructure-based variable speed limit systems are not strictly followed at all times by other drivers. Our designed, implemented, and deployed algorithm is able to follow variable speed limits when others also follow it, avoid collisions with vehicles ahead, and adapt to prevailing traffic when other motorists are traveling well above the posted speeds. The key is to reject unsafe speed recommendations from infrastructure-based traffic smoothing systems, based on real-time local traffic conditions observed by the vehicle under control. This solution is implemented and deployed on two control vehicles in heavy multi-lane highway congestion. The results include analysis from system design, and field tests that validate the system's performance using an existing Variable Speed Limit system as the external source for speed recommendations, and the on-board sensors of a stock Toyota Rav4 for inputs that estimate the prevailing speed of traffic around the vehicle under control.

Josh Tobin, the co-founder and CEO of machine learning tool provider Gantry, didn't want to believe it at first. But Tobin, who previously worked as a research scientist at OpenAI, eventually came to the conclusion that it was true: The end of traditional ML modeling is upon us. The idea that you didn't need to train a machine learning model anymore and can get better results by just using off-the-shelf models without any tuning on your own custom data seemed wrong to Tobin, who spent years learning how to build these systems. When he first heard of the idea after starting his ML tool business Gantry, which he co-founded in 2021 with fellow OpenAI alum Vicky Cheung, he didn't want to believe it. "The first four or five times I heard that, my thinking was like, okay, these companies just don't know what they're doing," Tobin said.

Scientists from the University of Bristol have developed a small robot to teach ants and in turn, the ants were able to teach others in a unique experiment with the potential to replace one day the way humans are taught. The team built the robot to emulate the behavior of rock ants that use one-to-one tuition. Whenever an ant finds a much better nest, it teaches the route to another and the transfer of knowledge goes on to the entire ant community. Here researchers replaced the teacher ant with a small robot and taught an ant in tandem running along to a new nest. The pupil ant was not only able to learn the route but also found its way back home and then led a tandem run with another ant to the new nest.

For soft continuum arms, visual servoing is a popular control strategy that relies on visual feedback to close the control loop. However, robust visual servoing is challenging as it requires reliable feature extraction from the image, accurate control models and sensors to perceive the shape of the arm, both of which can be hard to implement in a soft robot. This letter circumvents these challenges by presenting a deep neural network-based method to perform smooth and robust 3D positioning tasks on a soft arm by visual servoing using a camera mounted at the distal end of the arm. A convolutional neural network is trained to predict the actuations required to achieve the desired pose in a structured environment. Integrated and modular approaches for estimating the actuations from the image are proposed and are experimentally compared. A proportional control law is implemented to reduce the error between the desired and current image as seen by the camera. The model together with the proportional feedback control makes the described approach robust to several variations such as new targets, lighting, loads, and diminution of the soft arm. Furthermore, the model lends itself to be transferred to a new environment with minimal effort.

The following is an excerpt from Soonish by Kelly and Zach Weinersmith. Thanks to recent advances in robotics, computing, and other technologies, a small but growing number of scientists and engineers think robot-made housing might finally be possible. In fact, not only is it possible, it may be far better. Robotic construction may increase the speed of construction, improve its quality, and lower its price. There are a number of ways this could work, including giant gantries that behave something like 3D printers, and robotic arms on wheels that might directly replace construction workers.

The elevator doors snap shut behind Otto Solis and his fellow ironworkers. With a quick shudder, gears kick in for a rattling 90-second ascent through the concrete structure rising at the corner of Wilshire Boulevard and Figueroa Street in downtown Los Angeles. The men huddle in the confined space. Wearing hard hats, bandannas, kneepads and gloves, they look like gladiators ready to fight. Foreman Solis and his crew of 10 belong to a class of ironworkers known as rod busters.