Among approaches for provably safe reinforcement learning, Model Predictive Shielding (MPS) has proven effective at complex tasks in continuous, high-dimensional state spaces, by leveraging a to ensure safety when the learned policy attempts to take risky actions. However, while MPS can ensure safety both during and after training, it often hinders task progress due to the conservative and task-oblivious nature of backup policies.This paper introduces (DMPS), which optimizes reinforcement learning objectives while maintaining provable safety. DMPS employs a local planner to dynamically select safe recovery actions that maximize both short-term progress as well as long-term rewards. Crucially, the planner and the neural policy play a synergistic role in DMPS. When planning recovery actions for ensuring safety, the planner utilizes the neural policy to estimate long-term rewards, allowing it to beyond its short-term planning horizon. Conversely, the neural policy under training learns from the recovery plans proposed by the planner, converging to policies that are both and in practice.This approach guarantees safety during and after training, with bounded recovery regret that decreases exponentially with planning horizon depth. Experimental results demonstrate that DMPS converges to policies that rarely require shield interventions after training and achieve higher rewards compared to several state-of-the-art baselines.

Among approaches for provably safe reinforcement learning, Model Predictive Shielding (MPS) has proven effective at complex tasks in continuous, high-dimensional state spaces, by leveraging a backup policy to ensure safety when the learned policy attempts to take risky actions. However, while MPS can ensure safety both during and after training, it often hinders task progress due to the conservative and task-oblivious nature of backup policies.This paper introduces Dynamic Model Predictive Shielding (DMPS), which optimizes reinforcement learning objectives while maintaining provable safety. DMPS employs a local planner to dynamically select safe recovery actions that maximize both short-term progress as well as long-term rewards. Crucially, the planner and the neural policy play a synergistic role in DMPS. When planning recovery actions for ensuring safety, the planner utilizes the neural policy to estimate long-term rewards, allowing it to observe beyond its short-term planning horizon.

As more doctors and nurses leave the profession, providers are turning to AI technology to help bridge the coverage gap, especially among older Americans. Young drivers must be a certain age to get a license -- but it's less clear when older adults should hang up the keys. Between 2020 and 2021, the number of motor vehicle deaths involving adults 65 and older rose by 15%, exceeding 8,200 fatalities, according to data from the National Safety Council. Tina Paff, president of Bick's Driving School of Western Hills in Ohio, spoke with Fox News Digital about how to ensure safety on the road for aging drivers -- and how family members can recognize the potential warning signs. Paff heads up the Bick's Driver Rehabilitation Program, which evaluates older adults' driving skills to determine whether to recommend that they "retire" from operating vehicles.

United States President Joe Biden has issued a sweeping executive order to regulate the development of artificial intelligence (AI) amid growing concern about its potential impact on everything from national security to public health. "To realise the promise of AI and avoid the risk, we need to govern this technology," Biden said on Thursday. "In the wrong hands, AI can make it easier for hackers to exploit vulnerabilities in the software that makes our society run." The executive order includes a provision that developers of the most powerful AI models must notify the government of their work and share safety test results. It also calls on the National Institute of Standards and Technology to establish "rigorous standards" for testing AI prior to its release, the Department of Commerce to develop guidelines for identifying AI-generated content, and agencies funding "life science projects" to establish "strong new standards of biological synthesis screening" to ensure AI cannot engineer biohazards.

Richtech Robotics spokesman Timothy Tanksley and Richtech Robotics COO Phil Zheng joined'Fox & Friends Weekend' to show how his company's robot barista can serve coffee on FOX Square. A shopping mall in Ohio is integrating cutting-edge AI technology into its safety team in the form of a 400-pound robot security guard. "He's our secret agent man," Stacie Schmidt, vice president of marketing at Crocker Park told local media of the new security robot. Crocker Park is an open-air shopping mall located in Westlake - a suburban town located about 15 miles outside of Cleveland - which sees nearly 10 million visitors a year and is home to 1,000 residents in luxury apartments. This month, leaders of Crocker Park introduced SAM, a 420-pound, 5'1" autonomous robot that will patrol sidewalks and act as a "watchdog," according to a press release provided to Fox News Digital. "Our priority has always been to provide a safe and secure environment for everyone who visits our center, and the Knightscope robot will play a crucial role in enhancing our existing security measures," Sean Flanigan, vice president of security at Stark Enterprises, which owns Crocker Park. SAM, which was built by California-based robotics company Knightscope, uses 360-degree video streaming and recording video capabilities to monitor areas and alert authorities to any potential issues. The robot can work 24 hours a day, rain or shine. "[SAM's] AI algorithms enable it to detect anomalies and issue alerts to the on-site security team in real-time.

When Teslas are in self-driving mode, they've been recorded crossing into oncoming traffic and hitting parked cars. But what would happen if an 80,000-pound, 18-wheel driverless truck suddenly went off the rails? That's an experiment some California legislators aren't ready to run. They argue that the state Department of Motor Vehicles has so badly mishandled the driverless car industry that it can't be trusted to oversee big rigs barreling down the highways autonomously. AB 316 -- which would wrest control of driverless truck testing and deployment from the DMV and require human drivers in the cab for at least five years while a safety record is collected -- passed in the Assembly on Wednesday.

This paper considers enforcing safety and stability of dynamical systems in the presence of model uncertainty. Safety and stability constraints may be specified using a control barrier function (CBF) and a control Lyapunov function (CLF), respectively. To take model uncertainty into account, robust and chance formulations of the constraints are commonly considered. However, this requires known error bounds or a known distribution for the model uncertainty, and the resulting formulations may suffer from over-conservatism or over-confidence. In this paper, we assume that only a finite set of model parametric uncertainty samples is available and formulate a distributionally robust chance-constrained program (DRCCP) for control synthesis with CBF safety and CLF stability guarantees. To facilitate efficient computation of control inputs during online execution, we present a reformulation of the DRCCP as a second-order cone program (SOCP). Our formulation is evaluated in an adaptive cruise control example in comparison to 1) a baseline CLF-CBF quadratic programming approach, 2) a robust approach that assumes known error bounds of the system uncertainty, and 3) a chance-constrained approach that assumes a known Gaussian Process distribution of the uncertainty.

Connectivity technology has shown great potentials in improving the safety and efficiency of transportation systems by providing information beyond the perception and prediction capabilities of individual vehicles. However, it is expected that human-driven and autonomous vehicles, and connected and non-connected vehicles need to share the transportation network during the transition period to fully connected and automated transportation systems. Such mixed traffic scenarios significantly increase the complexity in analyzing system behavior and quantifying uncertainty for highly interactive scenarios, e.g., lane changing. It is even harder to ensure system safety when neural network based planners are leveraged to further improve efficiency. In this work, we propose a connectivity-enhanced neural network based lane changing planner. By cooperating with surrounding connected vehicles in dynamic environment, our proposed planner will adapt its planned trajectory according to the analysis of a safe evasion trajectory. We demonstrate the strength of our planner design in improving efficiency and ensuring safety in various mixed traffic scenarios with extensive simulations. We also analyze the system robustness when the communication or coordination is not perfect.

Loss of upper extremity motor control and function is an unremitting symptom in post-stroke patients. This would impose hardships on accomplishing their daily life activities. Supernumerary robotic limbs (SRLs) were introduced as a solution to regain the lost Degrees of Freedom (DoFs) by introducing an independent new limb. The actuation systems in SRL can be categorized into rigid and soft actuators. Soft actuators have proven advantageous over their rigid counterparts through intrinsic safety, cost, and energy efficiency. However, they suffer from low stiffness, which jeopardizes their accuracy. Variable Stiffness Actuators (VSAs) are newly developed technologies that have been proven to ensure accuracy and safety. In this paper, we introduce the novel Supernumerary Robotic Limb based on Variable Stiffness Actuators. Based on our knowledge, the proposed proof-of-concept SRL is the first that utilizes Variable Stiffness Actuators. The developed SRL would assist post-stroke patients in bi-manual tasks, e.g., eating with a fork and knife. The modeling, design, and realization of the system are illustrated. The proposed SRL was evaluated and verified for its accuracy via predefined trajectories. The safety was verified by utilizing the momentum observer for collision detection, and several post-collision reaction strategies were evaluated through the Soft Tissue Injury Test. The assistance process is qualitatively verified through standard user-satisfaction questionnaire.

How can we make a robot learn in the real world while ensuring safety? In this work, we show how it's possible to face this problem. The key idea to exploit domain knowledge and use the constraint definition to our advantage. Following our approach, it's possible to implement learning robotic agents that can explore and learn in an arbitrary environment while ensuring safety at the same time. Safety is a fundamental feature in real-world robotics applications: robots should not cause damage to the environment, to themselves, and they must ensure the safety of people operating around them.