Luna 9 was the first humanmade object to reach the moon in 1966. Breakthroughs, discoveries, and DIY tips sent six days a week. For nearly 60 years, the first humanmade object to successfully land on the moon has been missing. However, researchers may now be closer than ever to finding the Soviet Union's Luna 9 spacecraft. Using an advanced machine learning program, an international team of scientists believe they have finally narrowed down a list of finalists for Luna 9's location.

The rapid development of artificial intelligence (AI) has unearthed the potential to assist humans in controlling advanced technologies. Shared autonomy (SA) facilitates control by combining inputs from a human pilot and an AI copilot. In prior SA studies, the copilot is constantly active in determining the action played at each time step. This limits human autonomy that may have deleterious effects on performance. In general, the amount of helpful copilot assistance varies greatly depending on the task dynamics.

Private companies are testing new power systems for longer rover missions and future human lunar habitats. Breakthroughs, discoveries, and DIY tips sent every weekday. A future lunar lander bound for the dark side of the moon will carry along a piece of equipment that could make these missions a little bit brighter. The lander in question is operated by Firefly Aerospace, the first commercial company to successfully land and operate spacecraft on the moon. A LightPort wireless power receiver will be mounted atop the Firefly Blue Ghost lander's upper deck.Developed by Canadian aerospace startup Volta Space Technologies, the cargo plays a key role in Volta's ultimate goal: establishing a network of satellites that can wirelessly beam solar power to spacecraft on the lunar surface.

Monte Carlo Tree Search is a cornerstone algorithm for online planning, and its root-parallel variant is widely used when wall clock time is limited but best performance is desired. In environments with continuous action spaces, how to best aggregate statistics from different threads is an important yet underexplored question. In this work, we introduce a method that uses Gaussian Process Regression to obtain value estimates for promising actions that were not trialed in the environment. We perform a systematic evaluation across 6 different domains, demonstrating that our approach outperforms existing aggregation strategies while requiring a modest increase in inference time.

America's great journey in space is about to face its most consequential moment in half a century. Everyone agrees: It's a complete disaster. I. Artemis, We Have a Problem As you may have heard, NASA plans to send a crew of astronauts around the moon in early 2026, followed by a lunar landing in 2027. Or maybe you haven't heard. When I told one of my daughters about this plan to send people to the moon, she said, after a long silence: "But I thought we already sent a bunch of people there a long time ago." This is a standard response when I quiz people about Artemis, NASA's program to return to the moon, and this time to stay . It's named for Apollo's twin sister and the goddess of the moon and the hunt. The other day, I was in a gaggle with six neighbors, all highly informed professional people--two of them with long careers at the National Science Foundation--and none knew anything about Artemis except one thing: It's a plan to send people to Mars. Artemis is a moon mission. There is no Mars mission NASA has no Mars rocket, no Mars capsule, no Mars mission crew. What it does have is a very troubled moon program. Artemis faces fundamental engineering challenges that have called into question the program's basic architecture. Reconfiguring a mission this important is hard in the best of times, but the agency is being forced to do it during a year of unprecedented internal turmoil. A new administration always means turnover, but NASA has been in an uncontrolled spin every bit as alarming as the one Neil Armstrong famously pulled out of during in 1966. More than a year ago, President-elect Donald Trump nominated a billionaire entrepreneur and Elon Musk ally, Jared Isaacman, to become NASA administrator. It was an unconventional choice, but Isaacman drew support from many quarters in the space community. Then, right before Isaacman was poised for confirmation by the Senate, Trump and Musk had a nasty falling-out, and Trump yanked Isaacman's nomination. Since Inauguration Day, NASA had been run by acting administrator Janet Petro, a veteran agency official, and with Isaacman out, she remained in charge until one day in July when Trump suddenly named Secretary of Transportation Sean Duffy as interim administrator.

Deep reinforcement learning has achieved impressive success in control tasks. However, its policies, represented as opaque neural networks, are often difficult for humans to understand, verify, and debug, which undermines trust and hinders real-world deployment. This work addresses this challenge by introducing a novel approach for programmatic control policy discovery, called Multimodal Large Language Model-assisted Evolutionary Search (MLES). MLES utilizes multimodal large language models as programmatic policy generators, combining them with evolutionary search to automate policy generation. It integrates visual feedback-driven behavior analysis within the policy generation process to identify failure patterns and guide targeted improvements, thereby enhancing policy discovery efficiency and producing adaptable, human-aligned policies. Experimental results demonstrate that MLES achieves performance comparable to Proximal Policy Optimization (PPO) across two standard control tasks while providing transparent control logic and traceable design processes. This approach also overcomes the limitations of predefined domain-specific languages, facilitates knowledge transfer and reuse, and is scalable across various tasks, showing promise as a new paradigm for developing transparent and verifiable control policies.

NASA's Boss Just Shook Up the Agency's Plans to Land on the Moon Sean Duffy called out SpaceX for being "behind schedule" on a lunar lander and said he'd explore other options. NASA acting administrator Sean Duffy made two television appearances on Monday morning in which he shook up the space agency's plans to return humans to the moon. Speaking on Fox News, where the secretary of transportation frequently appears in his acting role as NASA chief, Duffy said SpaceX has fallen behind in its efforts to develop the Starship vehicle as a lunar lander. Duffy also indirectly acknowledged that NASA's projected target of a 2027 crewed lunar landing is no longer achievable. Accordingly, he said he intended to expand the competition to develop a lander capable of carrying humans down to the moon from lunar orbit and back.