Intercepting fast moving objects, by its very nature, is challenging because of its tight time constraints. This problem becomes further complicated in the presence of sensor noise because noisy sensors provide, at best, incomplete information, which results in a distribution over target states to be intercepted. Since time is of the essence, to hit the target, the planner must begin directing the interceptor, in this case a robot arm, while still receiving information. We introduce an tree-like structure, which is grown using kinodynamic motion primitives in state-time space. This tree-like structure encodes reachability to multiple goals from a single origin, while enabling real-time value updates as the target belief evolves and seamless transitions between goals. We evaluate our framework on an interception task on a 6 DOF industrial arm (ABB IRB-1600) with an onboard stereo camera (ZED 2i). A robust Innovation-based Adaptive Estimation Adaptive Kalman Filter (RIAE-AKF) is used to track the target and perform belief updates.

HE rapid advancement of uncrewed aerial vehicles (UA Vs) and their supporting infrastructure has significantly expanded the UA V market, enabling diverse applications such as aerial imaging, last-mile delivery, and air traffic management [1], [2]. To meet the demands of these complex tasks, modern UA Vs are increasingly equipped with autonomous modules for environmental perception, navigation, and obstacle avoidance. Despite these advances, UA Vs often fail to cope with sudden human-initiated attacks. Recent reports have documented cases where crowds at public events throw projectiles to disrupt UA V operations [3], [4], posing significant threats to their safety and public security. Consequently, there is an urgent need for robust strategies to counter human-initiated attacks involving fast-moving projectiles. Developing robust UA V systems capable of rapid responses to sudden human-initiated attacks remains a critical and unresolved research problem. Dodging such projectile threats involves overcoming several challenges: (1) Perception Latency: Projectiles often emerge suddenly at close range, leaving a narrow time window for detection and dodging. Therefore, minimizing the delay between sensing and control is crucial while maintaining high prediction accuracy to ensure effective avoidance.

Tehran, Iran – Iran's army and Islamic Revolutionary Guard Corps (IRGC) have been showcasing and testing new defensive and offensive weapons in large-scale military exercises for the past three months. The country is preparing for another tumultuous year amid threats by the United States and Israel to bomb Iranian nuclear facilities, critical energy infrastructure, and military sites. Iran is also promising a third iteration of its major military strikes on Israel, in retaliation for Israeli attacks amid the devastating war on Gaza. The exercises – Eqtedar, Zolfaqar and Great Prophet – have been held across Iran, the Sea of Oman and the northern Indian Ocean. The weapons tested show Iran intends to maintain its defiance of Israel and the West, refusing to negotiate with US President Donald Trump under his "maximum pressure" policy and continuing to advance its nuclear programme.

Most robotic behaviours focus on either manipulation or locomotion, where tasks that require the integration of both, such as full-body throwing, remain under-explored. Throwing with a robot involves complex coordination between object manipulation and legged locomotion, which is crucial for advanced real-world interactions. This work investigates the challenge of full-body throwing in robotic systems and highlights the advantages of utilising the robot's entire body. We propose a deep reinforcement learning (RL) approach that leverages the robot's body to enhance throwing performance through a strategically designed curriculum to avoid local optima and sparse but informative reward functions to improve policy flexibility. The robot's body learns to generate additional momentum and fine-tune the projectile release velocity. Our full-body method achieves on average 47% greater throwing distance and 34% greater throwing accuracy than the arm alone, across two robot morphologies - an armed quadruped and a humanoid. We also extend our method to optimise robot stability during throws. The learned policy effectively generalises throwing to targets at any 3D point in space within a specified range, which has not previously been achieved and does so with human-level throwing accuracy. We successfully transferred this approach from simulation to a real robot using sim2real techniques, demonstrating its practical viability.

Things to Do in L.A. Tap to enable a layout that focuses on the article. UC chancellors get big raises, putting them between $785,000 and nearly $1.2 million The UC regents approved pay raises for seven chancellors at their September meeting. At UC Irvine, above, the chancellor will earn $895,000 a year, effective this month. University of California chancellors will get big salary boosts -- near or exceeding 30% in most cases -- as the Board of Regents agreed Thursday that higher pay was needed to bring leaders of the nation's top public university system closer to what their peers earn. The increases, which will be paid through private sources rather than tuition dollars or state funding, are effective this month and will vary by campus.

Israel Defense Forces spokesperson Rear Adm. Daniel Hagari discusses Iran's attack on Israel, saying the attacks proved that Iran seeks to "escalate the region." JERUSALEM -- Some of Israel's most advanced military technology was on display over the weekend when its multi-level aerial defense array led the way in striking down an estimated 99% of the more than 350 drones, rockets and missiles that were fired by Iran in an unprecedented attack on the Jewish state. From the Iron Dome, which in its latest format uses artificial intelligence (AI) to improve accuracy when shooting short-range surface-to-surface rockets, to David's Sling, which intercepts short- to medium-range and medium- to long-range surface-to-surface missiles, to the Arrow 2 and 3 systems, which is used for longer-range ballistic and cruise missiles, as well as AI-driven aircraft and other technology, Israel's defensive operation proved it was far superior to the offensive capabilities of the Islamic Republic. In a press briefing following the attack, Israel Defense Forces spokesperson Rear Adm. Daniel Hagari hailed Israel's defensive operation, which was carried out together with partners from U.S. Central Command (CENTCOM), as a "very significant strategic achievement." He said it demonstrated the "exceptional professionalism" of Israel's Aerial Defense Array and the "defensive abilities of the air force as well as the army's military and technological superiority."

Houthi militants in Yemen are using Iranian-supplied missiles and drones to attack civilian and military targets across the Middle East, analysis from the Defense Intelligence Agency (DIA) shows. The report, "Iran: Enabling Houthi Attacks Across the Middle East," aims to provide more insight into the relationship between Iran and the Houthis. The militant group, stationed in Yemen, has for months been striking commercial vessels traveling through the Red Sea in protest of Palestinian civilians killed during Israel's ongoing offensive against Hamas members in Gaza. Houthi fighters stage a rally in support of the Palestinians in the Gaza Strip and against the U.S.-led airstrikes on Yemen, in Sanaa, Yemen, Monday, Jan. 29, 2024. Most recently, Houthi rebels fired ballistic missiles at two ships traveling through Middle East waters.

We are interested in studying sports with robots and starting with the problem of intercepting a projectile moving toward a robot manipulator equipped with a shield. To successfully perform this task, the robot needs to (i) detect the incoming projectile, (ii) predict the projectile's future motion, (iii) plan a minimum-time rapid trajectory that can evade obstacles and intercept the projectile, and (iv) execute the planned trajectory. These four steps must be performed under the manipulator's dynamic limits and extreme time constraints (<350ms in our setting) to successfully intercept the projectile. In addition, we want these trajectories to be smooth to reduce the robot's joint torques and the impulse on the platform on which it is mounted. To this end, we propose a kinodynamic motion planning framework that preprocesses smooth trajectories offline to allow real-time collision-free executions online. We present an end-to-end pipeline along with our planning framework, including perception, prediction, and execution modules. We evaluate our framework experimentally in simulation and show that it has a higher blocking success rate than the baselines. Further, we deploy our pipeline on a robotic system comprising an industrial arm (ABB IRB-1600) and an onboard stereo camera (ZED 2i), which achieves a 78% success rate in projectile interceptions.

In this work, we present Conditional Adversarial Latent Models (CALM), an approach for generating diverse and directable behaviors for user-controlled interactive virtual characters. Using imitation learning, CALM learns a representation of movement that captures the complexity and diversity of human motion, and enables direct control over character movements. The approach jointly learns a control policy and a motion encoder that reconstructs key characteristics of a given motion without merely replicating it. The results show that CALM learns a semantic motion representation, enabling control over the generated motions and style-conditioning for higher-level task training. Once trained, the character can be controlled using intuitive interfaces, akin to those found in video games.

The International Atomic Energy Agency (IAEA) stopping power database is a highly valued public resource compiling most of the experimental measurements published over nearly a century. The database-accessible to the global scientific community-is continuously updated and has been extensively employed in theoretical and experimental research for more than 30 years. This work aims to employ machine learning algorithms on the 2021 IAEA database to predict accurate electronic stopping power cross sections for any ion and target combination in a wide range of incident energies. Unsupervised machine learning methods are applied to clean the database in an automated manner. These techniques purge the data by removing suspicious outliers and old isolated values. A large portion of the remaining data is used to train a deep neural network, while the rest is set aside, constituting the test set. The present work considers collisional systems only with atomic targets. The first version of the ESPNN (electronic stopping power neural-network code), openly available to users, is shown to yield predicted values in excellent agreement with the experimental results of the test set.