Mayor blames ICE for'creating chaos' in Minneapolis and tells protesters to go home after illegal migrant is shot Iran confirms protest hero will NOT be executed as Trump reveals Tehran told him'the killing has stopped' after he threatened to take military action'Heads will roll!' US Marshals under fire for sending'Fugitive Task Force' to raid Timothy Busfield's mountain home 60 minutes after he turned himself in on child sex charges My Heated Rivalry fantasy became a reality... and my secret hookup will soon be leading the country Banks seize 367,000 homes as housing pain spreads across US... and it is about to get much worse Shocking truth about Minneapolis woman dragged from car by ICE while screaming that she was on her way to a doctor's appointment Inside Tiger Woods' 50th birthday bash as he cozies up to girlfriend Vanessa Trump to watch Bon Jovi Somali'fraudsters' force out YouTuber who exposed massive scandal amid fraud investigation in Minnesota Shameless Gwyneth ...

Travel chaos warning as hazardous'radiation fog' alert is issued in three states Real reason Bill Hader and Ali Wong's two-year relationship ended: Insiders reveal open secret about him in Hollywood... his cruel nickname... and his month from hell after Reiner murders horror It's madness NOT to annex Greenland: SCOTT JENNINGS spells out, as only he can, why America must act... before its enemies strike Kendall Jenner finally breaks silence on the rumors she's secretly a lesbian Real reason ICE refused to let medics rush to aid of Renee Nicole Good after she was shot dead in her car... as shocking video spread like wildfire The foods that actually block the body from gaining weight... even in people who eat high-fat diets Shocking study linking covid jabs and cancer'censored' by mysterious cyberattack Peppers will help protect you from the'super flu'... but which color you eat matters I gave up a middle-class family life at 40 to become an escort. Years later I discovered a common condition that affects so many women was to blame. Painful cause of death revealed for adorable child, 4, found dead in the woods two miles from dad's home Insiders reveal how the Reiner family decided to ax'despicable' Nick's legal fund: 'He's on his own' No nonsense uncle humiliates rude women for singing and talking during Broadway performance of Mamma Mia! - then has them thrown out of theater The REAL Princess Catherine: On her birthday, an intimate portrait of her marriage, how she finally solved the Meghan problem, her brave cancer fight... and a thrilling new rumor about her in America'Best medical drama ever' rockets up the Netflix charts as'broken' fans left sobbing by'perfect' ending after binge-watching every episode Inside NASA's high-stakes plan to evacuate astronauts from the ISS after medical emergency NASA is preparing to conduct its first-ever medical evacuation from the International Space Station (ISS), activating a contingency plan to return a crew to Earth months ahead of schedule. The plan, developed decades ago for medical emergencies in space, has never before been implemented during an ISS mission, agency officials said Thursday. Under the program, the returning astronauts will seal themselves inside the capsule, undock from the ISS, perform a controlled departure and reenter Earth's atmosphere for a parachute-assisted splashdown in the Pacific Ocean off the California coast.

The new video highlights the (cramped) life aboard the ISS. Breakthroughs, discoveries, and DIY tips sent every weekday. There is nearly 16,700 cubic feet of habitable area aboard the International Space Station (ISS). That makes it larger than a six-bedroom, two-bathroom house,but still small enough for a grand tour that takes less than 15 minutes. Earlier this month, NASA released a high-definition video showcase of the ISS, its facilities, and its crew recorded during the Crew-4 and Crew-5 missions in October 2022.

We address the problem of incremental semantic segmentation (ISS) recognizing novel object/stuff categories continually without forgetting previous ones that have been learned. The catastrophic forgetting problem is particularly severe in ISS, since pixel-level ground-truth labels are available only for the novel categories at training time. To address the problem, regularization-based methods exploit probability calibration techniques to learn semantic information from unlabeled pixels. While such techniques are effective, there is still a lack of theoretical understanding of them. Replay-based methods propose to memorize a small set of images for previous categories.

Abstract-- Reinforcement learning (RL) offers transforma-tive potential for robotic control in space. We present the first on-orbit demonstration of RL-based autonomous control of a free-flying robot, the NASA Astrobee, aboard the International Space Station (ISS). Using NVIDIA's Omniverse physics simulator and curriculum learning, we trained a deep neural network to replace Astrobee's standard attitude and translation control, enabling it to navigate in microgravity. This successful deployment demonstrates the feasibility of training RL policies terrestrially and transferring them to space-based applications. This paves the way for future work in In-Space Servicing, Assembly, and Manufacturing (ISAM), enabling rapid on-orbit adaptation to dynamic mission requirements. Future In-Space Servicing, Assembly, and Manufacturing (ISAM) missions require increasingly autonomous robotic systems capable of adapting to the dynamic and uncertain conditions of space.

Japan's HTV-X resupply vehicle arrives at the International Space Station where a robot arm operated by astronaut Kimiya Yui awaits early Thursday. Japan's newly developed HTV-X resupply vehicle arrived at the International Space Station in the small hours of Thursday Japan time. Japanese astronaut Kimiya Yui, 55, successfully caught the craft with a robotic arm around 12:58 a.m. and attached it to the ISS. "Thank you for entrusting me with this important task today," Yui said in communication with ground control soon after that. "Congratulations on the capture," fellow Japanese astronaut Akihiko Hoshide, 56, responded from the control room at NASA in the United States.

MIT astronauts aboard the International Space Station--and the MIT researchers who have sent up experiments--have advanced our understanding of science, space, and the universe. This image of the International Space Station and space shuttle Endeavour, flying at an altitude of approximately 350 kilometers, was taken by Expedition 27 crew member Paolo Nespoli from the Soyuz TMA-20 on May 24, 2011. On November 2, 2000, NASA astronaut Bill Shepherd, OCE '78, SM '78, and Russian cosmonauts Sergei Krikalev and Yuri Gidzenko made history as their Soyuz spacecraft docked with the International Space Station. The event marked the start of 25 years of continuous human presence in space aboard the ISS--a prolific period for space research. MIT-trained astronauts, scientists, and engineers have played integral roles in all aspects of the station's design, assembly, operations, and scientific research. One of MIT's most experienced NASA astronauts, Mike Fincke '89, is celebrating that milestone from space.

Abstract-- This paper presents a novel framework for analyzing Incremental-Input-to-State Stability (δISS) based on the idea of using rewards as "test functions." Whereas control theory traditionally deals with Lyapunov functions that satisfy a time-decrease condition, reinforcement learning (RL) value functions are constructed by exponentially decaying a Lipschitz reward function that may be non-smooth and unbounded on both sides. Thus, these RL-style value functions cannot be directly understood as Lyapunov certificates. We develop a new equivalence between a variant of incremental input-to-state stability of a closed-loop system under given a policy, and the regularity of RL-style value functions under adversarial selection of a H older-continuous reward function. This result highlights that the regularity of value functions, and their connection to incremental stability, can be understood in a way that is distinct from the traditional Lyapunov-based approach to certifying stability in control theory.

This paper proposes a novel theoretical framework for guaranteeing and evaluating the resilience of long short-term memory (LSTM) networks in control systems. We introduce "recovery time" as a new metric of resilience in order to quantify the time required for an LSTM to return to its normal state after anomalous inputs. By mathematically refining incremental input-to-state stability ($δ$ISS) theory for LSTM, we derive a practical data-independent upper bound on recovery time. This upper bound gives us resilience-aware training. Experimental validation on simple models demonstrates the effectiveness of our resilience estimation and control methods, enhancing a foundation for rigorous quality assurance in safety-critical AI applications.

We study the problem of imitating an expert demonstrator in a continuous state-and-action dynamical system. While imitation learning in discrete settings such as autoregressive language modeling has seen immense success and popularity in recent years, imitation in physical settings such as autonomous driving and robot learning has proven comparably more complex due to the compounding errors problem, often requiring elaborate set-ups to perform stably. Recent work has demonstrated that even in benign settings, exponential compounding errors are unavoidable when learning solely from expert-controlled trajectories, suggesting the need for more advanced policy parameterizations or data augmentation. To this end, we present minimal interventions that provably mitigate compounding errors in continuous state-and-action imitation learning. When the system is open-loop stable, we prescribe "action chunking," i.e., predicting and playing sequences of actions in open-loop; when the system is possibly unstable, we prescribe "noise injection," i.e., adding noise during expert demonstrations. These interventions align with popular choices in modern robot learning, though the benefits we derive are distinct from the effects they were designed to target. Our results draw insights and tools from both control theory and reinforcement learning; however, our analysis reveals novel considerations that do not naturally arise when either literature is considered in isolation.