Neural Information Processing Systems http://nips.cc/

No, the chatbot did not crash Perseverance. Since 2021, NASA's Perseverance rover has achieved a number of historic milestones, including sending back the first audio recordings from Mars . Now, nearly five years after landing on the Red Planet, it just achieved another feat. This past December, Perseverance successfully completed a route through a section of the Jezero crater plotted by Anthropic's Claude chatbot, marking the first time NASA has used a large language model to pilot the car-sized robot. Between December 8 and 10, Perseverance drove approximately 400 meters (about 437 yards) through a field of rocks on the Martian surface mapped out by Claude.

Vision-language models (VLMs) have exhibited impressive capabilities across diverse image understanding tasks, but still struggle in settings that require reasoning over extended sequences of camera frames from a video. This limits their utility in embodied settings, which require reasoning over long frame sequences from a continuous stream of visual input at each moment of a task attempt. To address this limitation, we propose ROVER (Reasoning Over VidEo Recursively), a framework that enables the model to recursively decompose long-horizon video trajectories into segments corresponding to shorter subtasks within the trajectory. In doing so, ROVER facilitates more focused and accurate reasoning over temporally localized frame sequences without losing global context. We evaluate ROVER, implemented using an in-context learning approach, on diverse OpenX Embodiment videos and on a new dataset derived from RoboCasa that consists of 543 videos showing both expert and perturbed non-expert trajectories across 27 robotic manipulation tasks. ROVER outperforms strong baselines across three video reasoning tasks: task progress estimation, frame-level natural language reasoning, and video question answering. We observe that, by reducing the number of frames the model reasons over at each timestep, ROVER mitigates hallucinations, especially during unexpected or non-optimal moments of a trajectory. In addition, by enabling the implementation of a subtask-specific sliding context window, ROVER's time complexity scales linearly with video length, an asymptotic improvement over baselines. Demos, code, and data available at: https://rover-vlm.github.io

NASA's Perseverance rover discovers a shiny metallic rock nicknamed 'Phippsaksla' on Mars that scientists believe could be a meteorite from an ancient asteroid.

They're rare events, but the results are dramatic. Breakthroughs, discoveries, and DIY tips sent every weekday. Mars receives its fair share of cosmic collisions . With less than one percent the atmosphere as Earth, some meteoroids fail to burn up entirely before reaching the Red Planet's surface. When they do, they can usher dramatic changes to the barren Martian landscape that stretch for miles.

Abstract-- The European Space Agency (ESA) and the European Space Resources Innovation Centre (ESRIC) created the Space Resources Challenge to invite researchers and companies to propose innovative solutions for Multi-Robot Systems (MRS) space prospection. This paper proposes the Resilient Exploration And Lunar Mapping System 2 (REALMS2), a MRS framework for planetary prospection and mapping. Based on Robot Operating System version 2 (ROS 2) and enhanced with Visual Simultaneous Localisation And Mapping (vSLAM) for map generation, REALMS2 uses a mesh network for a robust ad hoc network. This system is designed for heterogeneous multi-robot exploratory missions, tackling the challenges presented by extraterrestrial environments. REALMS2 was used during the second field test of the ESA-ESRIC Challenge and allowed to map around 60% of the area, using three homogeneous rovers while handling communication delays and blackouts. Recently, the Moon has regained the focus of space agencies and private companies for potential In-Situ Resources Utilisation (ISRU). Therefore, the European Space Agency (ESA) and the European Space Resources Innovation Centre (ESRIC) seek to increase the level of autonomy of robotic systems used for the exploration of space resources. ESA and ESRIC organised the Space Resources Challenge [1], where 13 research teams competed in a first field test to demonstrate their concepts of autonomous systems, leveraging the advantages of Multi-Robot Systems (MRS). The five best teams continued to a second field test [2] with the task of finding different resources within a large lunar analogue environment, shown in Fig 1. During the first field test of the Challenge [2], the authors present the Resilient Exploration And Lunar Mapping System (REALMS) [3], a MRS using two rovers mapping the environment with Visual Simultaneous Localisation And Mapping (vSLAM). This research was funded in whole, or in part, by the Luxembourg National Research Fund (FNR), grant references 14783405, 17025341 and 17679211.

Autonomous navigation in unstructured environments is essential for field and planetary robotics, where robots must efficiently reach goals while avoiding obstacles under uncertain conditions. Conventional algorithmic approaches often require extensive environment-specific tuning, limiting scalability to new domains. Deep Reinforcement Learning (DRL) provides a data-driven alternative, allowing robots to acquire navigation strategies through direct interactions with their environment. This work investigates the feasibility of DRL policy generalization across visually and topographically distinct simulated domains, where policies are trained in terrestrial settings and validated in a zero-shot manner in extraterrestrial environments. A 3D simulation of an agricultural rover is developed and trained using Proximal Policy Optimization (PPO) to achieve goal-directed navigation and obstacle avoidance in farmland settings. The learned policy is then evaluated in a lunar-like simulated environment to assess transfer performance. The results indicate that policies trained under terrestrial conditions retain a high level of effectiveness, achieving close to 50\% success in lunar simulations without the need for additional training and fine-tuning. This underscores the potential of cross-domain DRL-based policy transfer as a promising approach to developing adaptable and efficient autonomous navigation for future planetary exploration missions, with the added benefit of minimizing retraining costs.