An AI-powered robot was able to remove a gall bladder from a dead pig in what researchers claim is the first realistic surgery by a machine with almost no human intervention. The robot is powered by a two-tier AI system trained on 17 hours of video encompassing 16,000 motions made in operations by human surgeons. When put to work, the first layer of the AI system watches video from an endoscope monitoring the surgery and issues plain-language instructions, such as "clip the second duct", while the second AI layer turns each instruction into three-dimensional tool motions. In all, the gall bladder surgery required 17 separate tasks. The robotic system performed the operation eight times, achieving 100 per cent success in all of the tasks.

Efficient material logistics play a critical role in controlling costs and schedules in the construction industry. However, manual material handling remains prone to inefficiencies, delays, and safety risks. Autonomous forklifts offer a promising solution to streamline on-site logistics, reducing reliance on human operators and mitigating labor shortages. This paper presents the development and evaluation of the Autonomous Dynamic All-terrain Pallet Transporter (ADAPT), a fully autonomous off-road forklift designed for construction environments. Unlike structured warehouse settings, construction sites pose significant challenges, including dynamic obstacles, unstructured terrain, and varying weather conditions. To address these challenges, our system integrates AI-driven perception techniques with traditional approaches for decision making, planning, and control, enabling reliable operation in complex environments. We validate the system through extensive real-world testing, comparing its long-term performance against an experienced human operator across various weather conditions. We also provide a comprehensive analysis of challenges and key lessons learned, contributing to the advancement of autonomous heavy machinery. Our findings demonstrate that autonomous outdoor forklifts can operate near human-level performance, offering a viable path toward safer and more efficient construction logistics.

The agricultural industry is undergoing a significant transformation with the increasing adoption of autonomous technologies. Addressing complex challenges related to safety and security, components and validation procedures, and liability distribution is essential to facilitate the adoption of autonomous technologies. This paper explores the collaborative groups and initiatives undertaken to address these challenges. These groups investigate inter alia three focal topics: 1) describe the functional architecture of the operational range, 2) define the work context, i.e., the realistic scenarios that emerge in various agricultural applications, and 3) the static and dynamic detection cases that need to be detected by sensor sets. Linked by the Agricultural Operational Design Domain (Agri-ODD), use case descriptions, risk analysis, and questions of liability can be handled. By providing an overview of these collaborative initiatives, this paper aims to highlight the joint development of autonomous agricultural systems that enhance the overall efficiency of farming operations.

Deep reinforcement learning has gradually shown its latent decision-making ability in urban rail transit autonomous operation. However, since reinforcement learning can not neither guarantee safety during learning nor execution, this is still one of the major obstacles to the practical application of reinforcement learning. Given this drawback, reinforcement learning applied in the safety-critical autonomous operation domain remains challenging without generating a safe control command sequence that avoids overspeed operations. Therefore, a SSA-DRL framework is proposed in this paper for safe intelligent control of urban rail transit autonomous operation trains. The proposed framework is combined with linear temporal logic, reinforcement learning and Monte Carlo tree search and consists of four mainly module: a post-posed shielding, a searching tree module, a DRL framework and an additional actor. Furthermore, the output of the framework can meet speed constraint, schedule constraint and optimize the operation process. Finally, the proposed SSA-DRL framework for decision-making in urban rail transit autonomous operation is evaluated in sixteen different sections, and its effectiveness is demonstrated through an ablation experiment and comparison with the scheduled operation plan.

Abstract-- Establishing lunar infrastructure is paramount to long-term habitation on the Moon. To meet the demand for future lunar infrastructure development, we present Crater-Grader, a novel system for autonomous robotic earthmoving tasks within lunar constraints. In contrast to the current approaches to construction autonomy, CraterGrader uses online perception for dynamic mapping of deformable terrain, devises an energy-efficient material movement plan using an optimization-based transport planner, precisely localizes without GPS, and uses integrated drive and tool control to manipulate regolith with unknown and non-constant geotechnical parameters. We demonstrate CraterGrader's ability to achieve unprecedented performance in autonomous smoothing and grading within a lunar-like environment, showing that this framework is capable, robust, and a benchmark for future planetary site preparation robotics. Robotic systems show promise in constructing surface infrastructure in aerospace applications, including of the manifested robotic system, emphasizing its potential landing pads, roads, structural foundations, trenches, and applications in lunar infrastructure development.

Despite our intrinsic distrust of AI in space taught to us by movies like 2001: A Space Odyssey ("I'm afraid I can't do that, Dave"), it offers large advantages to both manned and unmanned missions. To that end, NASA is developing a system that will allow astronauts to perform maneuvers, conduct experiments and more using a natural-language ChatGPT-like interface, The Guardian reported. "The idea is to get to a point where we have conversational interactions with space vehicles and they [are] also talking back to us on alerts, interesting findings they see in the solar system and beyond," said Dr. Larissa Suzuki, speaking at an IEEE meeting on next-gen space communication. NASA aims to deploy the system on its Lunar Gateway, a space station that will orbit the Moon and provide support for NASA's Artemis mission. It would use a natural language interface that allows astronauts to seek advice on experiments or conduct maneuvers without diving into complex manuals. On a dedicated page soliciting small business support for Lunar Gateway, NASA wrote that it would require AI and machine learning technologies to manage various systems when it's unoccupied as well.

This paper describes Waymo's Collision Avoidance Testing (CAT) methodology: a scenario-based testing method that evaluates the safety of the Waymo Driver Automated Driving Systems' (ADS) intended functionality in conflict situations initiated by other road users that require urgent evasive maneuvers. Because SAE Level 4 ADS are responsible for the dynamic driving task (DDT), when engaged, without immediate human intervention, evaluating a Level 4 ADS using scenario-based testing is difficult due to the potentially infinite number of operational scenarios in which hazardous situations may unfold. To that end, in this paper we first describe the safety test objectives for the CAT methodology, including the collision and serious injury metrics and the reference behavior model representing a non-impaired eyes on conflict human driver used to form an acceptance criterion. Afterward, we introduce the process for identifying potentially hazardous situations from a combination of human data, ADS testing data, and expert knowledge about the product design and associated Operational Design Domain (ODD). The test allocation and execution strategy is presented next, which exclusively utilize simulations constructed from sensor data collected on a test track, real-world driving, or from simulated sensor data. The paper concludes with the presentation of results from applying CAT to the fully autonomous ride-hailing service that Waymo operates in San Francisco, California and Phoenix, Arizona. The iterative nature of scenario identification, combined with over ten years of experience of on-road testing, results in a scenario database that converges to a representative set of responder role scenarios for a given ODD. Using Waymo's virtual test platform, which is calibrated to data collected as part of many years of ADS development, the CAT methodology provides a robust and scalable safety evaluation.

Advanced reactors to be deployed in the coming decades will face deregulated energy markets, and may adopt flexible operation to boost profitability. To aid in the transition from baseload to flexible operation paradigm, autonomous operation is sought. This work focuses on the control aspect of autonomous operation. Specifically, a hierarchical control system is designed to support constraint enforcement during routine operational transients. Within the system, data-driven modeling, physics-based state observation, and classical control algorithms are integrated to provide an adaptable and robust solution. A 320 MW Fluoride-cooled High-temperature Pebble-bed Reactor is the design basis for demonstrating the control system. The hierarchical control system consists of a supervisory layer and low-level layer. The supervisory layer receives requests to change the system's operating conditions, and accepts or rejects them based on constraints that have been assigned. Constraints are issued to keep the plant within an optimal operating region. The low-level layer interfaces with the actuators of the system to fulfill requested changes, while maintaining tracking and regulation duties. To accept requests at the supervisory layer, the Reference Governor algorithm was adopted. To model the dynamics of the reactor, a system identification algorithm, Dynamic Mode Decomposition, was utilized. To estimate the evolution of process variables that cannot be directly measured, the Unscented Kalman Filter, incorporating a nonlinear model of nuclear dynamics, was adopted. The composition of these algorithms led to a numerical demonstration of constraint enforcement during a 40 % power drop transient. Adaptability was demonstrated by modifying the constraint values, and enforcing them during the transient. Robustness was demonstrated by enforcing constraints under noisy environments.

Hear from top leaders discuss topics surrounding AL/ML technology, conversational AI, IVA, NLP, Edge, and more. Drones have been talked about extensively for two decades now. In many respects, that attention has been warranted. Military drones have changed the way we fight wars. Consumer drones have changed the way we film the world.

A reliable fault management system is essential for safely operating autonomous vehicle fleets for commercial customers, helping to pave the way to full commercialization. With this in mind, self-driving tech firm Aurora Innovation Inc recently delivered its Beta 3.0 product update and demonstrated its fault management system – specifically the Aurora Driver's ability to detect system issues and respond by safely pulling over to the side of the road without any human involvement. The company says it achieved its milestone ahead of schedule, following its implementation on Aurora-powered trucks operating on public roads at highway speeds. "Any of a number of factors, from blown tires to damaged sensors, can compromise a vehicle while on the road," stated Sterling Anderson, Aurora co-founder and chief product officer. "Safely detecting and responding to those issues is essential for a reliable self-driving product operating at scale. Our fault management system lays the groundwork for safe autonomous operations without vehicle operators, chase vehicles or remote human fallback systems."