The human ability to seamlessly perform multimodal reasoning and physical interaction in the open world is a core goal for general-purpose embodied intelligent systems. Recent vision-language-action (VLA) models, which are co-trained on large-scale robot and visual-text data, have demonstrated notable progress in general robot control. However, they still fail to achieve human-level flexibility in interleaved reasoning and interaction. In this work, introduce EO-Robotics, consists of EO-1 model and EO-Data1.5M dataset. EO-1 is a unified embodied foundation model that achieves superior performance in multimodal embodied reasoning and robot control through interleaved vision-text-action pre-training. The development of EO-1 is based on two key pillars: (i) a unified architecture that processes multimodal inputs indiscriminately (image, text, video, and action), and (ii) a massive, high-quality multimodal embodied reasoning dataset, EO-Data1.5M, which contains over 1.5 million samples with emphasis on interleaved vision-text-action comprehension. EO-1 is trained through synergies between auto-regressive decoding and flow matching denoising on EO-Data1.5M, enabling seamless robot action generation and multimodal embodied reasoning. Extensive experiments demonstrate the effectiveness of interleaved vision-text-action learning for open-world understanding and generalization, validated through a variety of long-horizon, dexterous manipulation tasks across multiple embodiments. This paper details the architecture of EO-1, the data construction strategy of EO-Data1.5M, and the training methodology, offering valuable insights for developing advanced embodied foundation models.

In recent years, the concept of artificial intelligence (AI) has emerged from the annals of science fiction into everyday life, as society grapples with a range of technological issues from cyber security to driverless cars. Along with other terms, such as machine learning, neural networks and'the Turing test', AI has become a contemporary media buzzword in both fact and fiction – despite a general lack of understanding of what AI really means to the average citizen of Earth. Out in space, however, the use of AI is arguably more mature and well-understood, at least for current applications, while its integration into future manned space exploration is pretty much a'no-brainer', along with sibling technologies such as robotics, telepresence and autonomous systems. Most of us, if asked to consider the link between AI and space, would probably think of HAL, the miscreant computer from '2001: A Space Odyssey', rather than any real-life application. Certainly, it seems to tick the box for any current definition of AI, which typically involves'computer systems able to perform tasks normally requiring human intelligence'.

NASA's highly experimental Earth Observing-1 satellite mission was supposed to last just a year. It did that, and then survived 16 more--all the while testing NASA's riskiest, oddball ideas. It's been a proving ground for everything from multi- and hyperspectral imagers, to a self-piloting AI. But EO-1 is finally out of fuel, and at the end of the month the craft's operating team will close up shop. Already out of fuel, EO-1 itself will continue to slowly shuffle off its orbital coil until it burns up in Earth's atmosphere.

The recent eruption of Erta Ale volcano in northeastern Ethiopia highlights the speed and impact of space artificial intelligence (A.I.). One of our planet's few exposed lava lakes is changing, and artificial intelligence is helping NASA understand how. On January 21, a fissure opened at the top of Ethiopia's Erta Ale volcano --one of the few in the world with an active lava lake in its caldera. Volcanologists sent out requests for NASA's Earth Observing 1 (EO-1) spacecraft to image the eruption, which was large enough to begin reshaping the volcano's summit. As it turned out, that spacecraft was already busy collecting data of the lava lake.

We describe a timeline-based scheduling algorithm developed for mission operations of the EO-1 earth observing satellite. We first describe the range of operational constraints for operations focusing on maneuver and thermal constraints that cannot be modeled in typical planner/schedulers. We then describe a greedy heuristic scheduling algorithm and compare its performance to both the prior scheduling algorithm - documenting an over 50% increase in scenes scheduled with estimated value of millions of dollars US. We also compare to a relaxed optimal scheduler showing that the greedy scheduler produces schedules with scene count within 15% of an upper bound on optimal schedules.