NASA's Perseverance Rover Completes First AI-Planned Drive on Mars Using Claude AI Model

NASA's Perseverance Rover Completes First AI-Planned Drive on Mars - NASA

The team for the six-wheeled scientist used a vision-capable AI to create a safe route over the Red Planet's surface without the input of human route planners. NASA's Perseverance Mars rover has completed the first drives on another world that were planned by artificial intelligence. Executed on Dec. 8 and 10, and led by the agency's Jet Propulsion Laboratory in Southern California, the demonstration used generative AI to create waypoints for Perseverance, a complex decision-making task typically performed manually by the mission's human rover planners. "This demonstration shows how far our capabilities have advanced and broadens how we will explore other worlds," said NASA Administrator Jared Isaacman. "Autonomous technologies like this can help missions to operate more efficiently, respond to challenging terrain, and increase science return as distance from Earth grows. It's a strong example of teams applying new technology carefully and responsibly in real operations." During the demonstration, the team leveraged a type of generative AI called vision-language models to analyze existing data from JPL's surface mission dataset. The AI used the same imagery and data that human planners rely on to generate waypoints -- fixed locations where the rover takes up a new set of instructions -- so that Perseverance could safely navigate the challenging Martian terrain. The initiative was led out of JPL's Rover Operations Center (ROC) in collaboration with Anthropic, using the company's Claude AI models. Mars is on average about 140 million miles (225 million kilometers) away from Earth. This vast distance creates a significant communication lag, making real-time remote operation -- or "joy-sticking" -- of a rover impossible. Instead, for the past 28 years, over several missions, rover routes have been planned and executed by human "drivers," who analyze the terrain and status data to sketch a route using waypoints, which are usually spaced no more than 330 feet (100 meters) apart to avoid any potential hazards. Then they send the plans via NASA's Deep Space Network to the rover, which executes them. But for Perseverance's drives on the 1,707 and 1,709 Martian days, or sols, of the mission, the team did something different: Generative AI provided the analysis of the high-resolution orbital imagery from the HiRISE (High Resolution Imaging Science Experiment) camera aboard NASA's Mars Reconnaissance Orbiter and terrain-slope data from digital elevation models. After identifying critical terrain features -- bedrock, outcrops, hazardous boulder fields, sand ripples, and the like -- it generated a continuous path complete with waypoints. To ensure the AI's instructions were fully compatible with the rover's flight software, the engineering team also processed the drive commands through JPL's "digital twin" (virtual replica of the rover), verifying over 500,000 telemetry variables before sending commands to Mars. On Dec. 8, with generative AI waypoints in its memory, Perseverance drove 689 feet (210 meters). Two days later, it drove 807 feet (246 meters). "The fundamental elements of generative AI are showing a lot of promise in streamlining the pillars of autonomous navigation for off-planet driving: perception (seeing the rocks and ripples), localization (knowing where we are), and planning and control (deciding and executing the safest path)," said Vandi Verma, a space roboticist at JPL and a member of the Perseverance engineering team. "We are moving towards a day where generative AI and other smart tools will help our surface rovers handle kilometer-scale drives while minimizing operator workload, and flag interesting surface features for our science team by scouring huge volumes of rover images." "Imagine intelligent systems not only on the ground at Earth, but also in edge applications in our rovers, helicopters, drones, and other surface elements trained with the collective wisdom of our NASA engineers, scientists, and astronauts," said Matt Wallace, manager of JPL's Exploration Systems Office. "That is the game-changing technology we need to establish the infrastructure and systems required for a permanent human presence on the Moon and take the U.S. to Mars and beyond." Managed for NASA by Caltech, JPL is home to the Rover Operations Center (ROC). It also manages operations of the Perseverance rover on behalf of the agency's Science Mission Directorate as part of NASA's Mars Exploration Program portfolio.

NASA taps Claude to conjure Mars rover's travel plan

Anthropic's Claude machine learning model has boldly planned what no Claude has planned before - a path across Mars for NASA's Perseverance rover. Perseverance traveled about 400 meters on the Martian surface last month based on an AI-generated path. It did so with the blessing of engineers at NASA's Jet Propulsion Laboratory (JPL), who decided to delegate the meticulous work of route planning to Anthropic's AI model. As Anthropic explains in its writeup of the milestone, the surface of Mars can be treacherous for rovers. No one wants to be responsible for getting pricey space kit stuck in the sand, as happened with the Spirit rover in 2009. So the Perseverance team spends a fair amount of time on route planning. This involves consulting orbital and surface imagery of Mars in order to set a series of waypoints to guide the rover's movements. Once plotted, this data gets transmitted about 140 million miles or 225 million kilometers - the average distance from Earth to Mars - where it's received by Perseverance as a navigational plan. Live-driving via joystick isn't feasible given the distance involved. Perseverance has an AutoNav system that handles real-time decision making. "AutoNav allows the rover to autonomously re-plan its route around rocks or other obstacles on its way to a pre-established destination," NASA explains. The re-planning may not be needed if the pre-planning went well. The pre-planning is "time-consuming" and "laborious," as Anthropic puts it, so JPL researchers decided to let Claude - using its vision capabilities - have a go. "Generative AI provided the analysis of the high-resolution orbital imagery from the HiRISE (High Resolution Imaging Science Experiment) camera aboard NASA's Mars Reconnaissance Orbiter and terrain-slope data from digital elevation models," JPL said in an online post. "After identifying critical terrain features - bedrock, outcrops, hazardous boulder fields, sand ripples, and the like - it generated a continuous path complete with waypoints." Claude generated Rover commands in Rover Markup Language (RML), which is based on XML. The version of Claude available on the web could not emit RML when asked and initially denied any knowledge of RML. When pointed to Anthropic's statement on the matter, Claude responded, "You're absolutely right, and I apologize for my initial response!" Nonetheless, Claude could not provide an example of RML, a shortcoming that the model attributed to the lack of a publicly documented standard. But Claude evidently did generate RML when it had access to NASA's data. And that's when the humans took the opportunity to check the route plan. AI models make mistakes and, even if that weren't a concern, that's just the sort of thing one does when programming rovers on other planets. Using a simulator representing a virtual replica of the rover, JPL engineers checked more than 500,000 telemetry variables about the rover's projected position and potential obstacles. And they made corrections. "When the JPL engineers reviewed Claude's plans, they found that only minor changes were needed," Anthropic said. "For instance, ground-level camera images (which Claude hadn't seen) gave a clearer view of sand ripples on either side of a narrow corridor; the rover drivers elected to split the route more precisely than Claude had at this point. But otherwise, the route held up well. The plans were sent to Mars, and the rover successfully traversed the planned path." On Martian days (sols) 1,707 and 1,709 (starting from the landing date 18 February 2021 at 08.55pm GMT), which corresponded to December 8 and December 10, 2025, Perseverance executed routes planned by AI instead of humans. The rover didn't follow the set route exactly. NASA's image of the December 10 path shows that the pre-planned route and the actual route differ slightly, presumably based on decisions made by the AutoNav system. But AI played a role, one many models can be expected to reprise as vision-language-actions models become more capable and get stuffed into robots. "This demonstration shows how far our capabilities have advanced and broadens how we will explore other worlds," said NASA Administrator Jared Isaacman in a statement. "Autonomous technologies like this can help missions to operate more efficiently, respond to challenging terrain, and increase science return as distance from Earth grows. It's a strong example of teams applying new technology carefully and responsibly in real operations." Anthropic reports that JPL engineers say Claude can cut the time required for route planning in half. The AI biz however failed specify the amount of time being halved. Representatives of Anthropic and JPL couldn't immediately be reached to quantify that fraction. ®

NASA used Claude to plot a route for its Perseverance rover on Mars

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. As you might imagine, using an AI model to plot a course for Perseverance wasn't as simple as inputting a single prompt. As NASA explains, routing Perseverance is no easy task, even for a human. "Every rover drive needs to be carefully planned, lest the machine slide, tip, spin its wheels, or get beached," NASA said. "So ever since the rover landed, its human operators have painstakingly laid out waypoints -- they call it a 'breadcrumb trail' -- for it to follow, using a combination of images taken from space and the rover's onboard cameras." To get Claude to complete the task, NASA had to first provide Claude Code, Anthropic's programming agent, with the "years" of contextual data from the rover before the model could begin writing a route for Perseverance. Claude then went about the mapping process methodically, stringing together waypoints from ten-meter segments it would later critique and iterate on. This being NASA we're talking about, engineers from the agency's Jet Propulsion Laboratory (JPL) made sure to double check the model's work before sending it to Perseverance. The JPL team ran Claude's waypoints through a simulation they use every day to confirm the accuracy of commands sent to the rover. In the end, NASA says it only had to make "minor changes" to Claude's route, with one tweak coming as a result of the fact the team had access to ground-level images Claude hadn't seen in its planning process. "The engineers estimate that using Claude in this way will cut the route-planning time in half, and make the journeys more consistent," NASA said. "Less time spent doing tedious manual planning -- and less time spent training -- allows the rover's operators to fit in even more drives, collect even more scientific data, and do even more analysis. It means, in short, that we'll learn much more about Mars." While the productivity gains offered by AI are often overstated, in the case of NASA, any tool that could allow its scientists to be more efficient is sure to be welcome. Over the summer, the agency lost about 4,000 employees - accounting for about 20 percent of its workforce - due to Trump administration cuts. Going into 2026, the president had proposed gutting the agency's science budget by nearly half before Congress ultimately rejected that plan in early January. Still, even with its funding preserved just below 2025 levels, the agency has a tough road ahead. It's being asked to return to the Moon with less than half the workforce it had during the height of the Apollo program. For Anthropic, meanwhile, this is a major feat. You may recall last spring Claude couldn't even beat Pokémon Red. In less than a year, the company's models have gone from struggling to navigate a simple 8-bit Game Boy game to successfully plotting a course for a rover on a distant planet. NASA is excited about the possibility of future collaborations, saying "autonomous AI systems could help probes explore ever more distant parts of the solar system."