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Rocket Lab’s first step towards SpaceX-style rocket reuse set for next Electron launch
Just over a year ago, Rocket Lab announced intentions to recover the first-stage of its small Electron launch vehicle, potentially making it the second private company on Earth – after SpaceX – to attempt to recover and reuse an orbital-class rocket.
In a media call earlier this week, Rocket Lab founder and CEO, Peter Beck, revealed that the first recovery attempt has been expedited to mid-November and will occur following the next flight of Rocket Lab’s Electron rocket.
Like competitor SpaceX, Rocket Lab aims to recover its first stage Electron booster to decrease production time and increase launch cadence. Rocket Lab now has three launchpads to launch from and is licensed by the Federal Aviation Administration to carry out up to 130 launches per calendar year. In order to increase the launch cadence of the Electron, production times need to decrease. This can effectively be accomplished with the recovery, refurbishment, and reuse of the small, carbon composite rocket booster.
Recovery Doesn’t Happen Overnight
Initially, the first step of recovering an expended first stage – a guided and controlled soft water landing under a parachute and retrieval by sea-vessel – was intended for the seventeenth launch of the Electron prior to the end of this calendar year. However, Rocket Lab is now targeting the sixteenth launch for the first recovery attempt, a mission appropriately nicknamed “Return to Sender.” When asked what prompted the move to an earlier launch, Beck stated to reporters, “the guys got it done in time. With a new development like this, it’s always very dependent on how the program runs and the program ran very successfully.”
Rocket Lab has been working toward this recovery attempt for quite some time. In late 2018, Rocket Lab began collecting data during launches to inform future recovery efforts and determine whether or not it would even be feasible with a small-class rocket. The first major block upgrade of the Electron booster debuted on the tenth flight, “Running Out of Fingers,” in December 2019.
The first recovery milestone, a task Beck called getting through “the wall,” was achieved following the tenth flight. And again in January 2020 following a successful eleventh flight of Electron. The “wall” Beck refers to is the Earth’s atmosphere. Returning a booster through the atmosphere intact requires extreme precision in terms of re-entry orientation and how efficient the heat shield is.
Because the Electron is a small-class rocket, Rocket Lab was able to collect enough data from previous flights to determine that the carbon composite frame could withstand a fall through the atmosphere given a precise enough angle of attack to sufficiently distribute thermal loads. According to Beck, the process is referred to as an “aero thermal decelerator.”
Small Rocket Following in Big Footsteps
SpaceX, Elon Musk’s space exploration company pioneered booster landing, recovery, and reuse efforts when the first Falcon 9 booster to successfully land returned to Landing Zone 1 at Cape Canaveral Air Force Station in Florida on December 21, 2015. SpaceX approaches the process of booster re-entry in a different way than what Rocket Lab has decided to attempt with Electron.
The Falcon 9 boosters perform a re-orientation flip and use the engines to perform what is known as a boost-back burn to set the rocket on the path to return to the Earth’s surface. The rocket then autonomously deploys titanium grid-fins that essentially steer, and slow the booster down as it falls through the atmosphere. Finally, the engines are re-ignited during a series of burns, and landing legs are deployed to propulsively land either at sea aboard an autonomous spaceport droneship or back on land at a landing zone.
The booster of Rocket Lab’s tenth mission in 2019 was outfitted with guidance and navigation hardware and cold gas attitude control thrusters used to flip and orient the booster to withstand the stresses of re-entry. Otherwise, no other hardware was incorporated to reduce the stresses of re-entry or slow the vehicle as it fell through the atmosphere. The booster made it through “the wall” intact and eventually slowed to a rate less than 900km per hour by the time it reached sea-level for an expected impact.
Eventually, Rocket Lab imagines its small Electron booster to be caught during a controlled descent under parachute canopy with a specially equipped helicopter and grappling hook. Beck and his team spent weeks outfitting a test article with prototype parachutes that were manufactured in-house.
A low-altitude drop test of a test article to simulate an Electron first stage was performed and a helicopter was able to snag the test article mid-air and deliver it one piece. Essentially, this proved that the concept was at least feasible and the small-class rocket could in fact be fully recovered to eventually be refurbished and reused. Since the completion of this drop test in April of 2020, the parachute design has been reevaluated and many more drop tests have been conducted. The final drop test with a more traditional system of a drogue parachute and an 18m ringsail type main parachute occurred in August of 2020 with a first stage simulator.
Next up, Rocket Lab plans to use the finalized design of the parachute system to bring Electron home safely for a soft landing in the Pacific Ocean. After which the booster will be collected by a recovery vessel, similar to the process that SpaceX uses to scoop its payload fairings from the water.
“Bringing a whole first stage back intact is the ultimate goal, but success for this mission is really about gaining more data, particularly on the drogue and parachute deployment system,” said Beck. With the parachute system verified the teams should be able to make any further iterations for a full capture and recovery effort on a future mission relatively quickly.
Rocket Lab will try to fully recover the “Return to Sender” expended first-stage booster once it separates approximately two and a half minutes after liftoff from Launch Complex 1 on the Mahia Penninsula of New Zealand. Electron will support a rideshare payload of thirty smallsats. The window to launch the sixteenth Electron mission opens on November 16 UTC (November 15 PT / ET). A hosted live webcast of the launch and recovery attempt will be provided on the company website approximately fifteen minutes prior to liftoff.
Tesla has launched a direct campaign targeting its customers in New Jersey, sending emails that warn of pending legislation that could effectively block true driverless technology in the state.
The email focuses on Senate Bill S.1677 and Assembly Bill A.3968, measures intended to create a three-year autonomous vehicle pilot program but laden with requirements that Tesla argues make unsupervised Robotaxis impossible.
Tesla is sending out this email to New Jersey Tesla owners, warning them that NJ could block autonomous vehicles, and to take action.
“Proposed legislation moving through Trenton right now would impose restrictions so severe that true driverless deployment would remain illegal.… pic.twitter.com/2bmY646AUL
— Sawyer Merritt (@SawyerMerritt) June 16, 2026
According to the email, the bills impose “restrictions so severe that true driverless deployment would remain illegal.” Specific hurdles include mandates for human safety drivers during operations, multimillion-dollar insurance minimums, reportedly $5 million, and thresholds like 100,000 miles of demonstrated safe autonomous driving before any driverless approval.
Tesla contends these are arbitrary barriers that ignore real-world performance data and favor entrenched competitors over innovative technologies like its Full Self-Driving (FSD) system.
The push comes as Tesla has started expanding Robotaxi operations in states like Texas, where unsupervised vehicles are already providing rides in several cities. New Jersey, by contrast, risks falling behind. The company highlights in the email communication that more than 94 percent of serious crashes result from human error, meaning impairment, distraction, or fatigue. These are all problems that Robotaxis eliminate entirely.
In 2025, New Jersey recorded 582 traffic deaths, underscoring the human cost of delayed adoption.
Tesla’s outreach stresses the transformative potential of robotaxis. For families, they could offer safer school runs without drowsy or distracted drivers. For seniors and people with disabilities, robotaxis promise independence and reliable mobility.
In areas with limited public transit, they could deliver affordable, on-demand transportation, reducing congestion, emissions, and overall transportation costs. Economically, the company warns that restrictive rules could cost New Jersey jobs, innovation investment, and billions in potential growth as autonomous ride-hailing scales elsewhere.
Supporters of the legislation, including Sen. Andrew Zwicker, describe the pilot as a cautious framework with strong safety oversight, including incident reporting, expert task forces, and restrictions in sensitive zones like school areas. They view it as balancing innovation with public protection.
Tesla and pro-AV advocates counter that the bill lacks technology neutrality, creates insurmountable entry barriers for commercial deployment, and prioritizes process over outcomes — effectively functioning as a de facto ban on services like Robotaxi.
This latest clash echoes Tesla’s past battles in New Jersey over direct vehicle sales. The email directs owners to Tesla’s advocacy platform, where they can send customized messages to legislators calling for amendments: outcome-based safety standards, open competition, and clear pathways for fully driverless commercial operations.
As hearings approach, Tesla’s campaign frames the issue as a choice between protecting the status quo and embracing life-saving progress. With robotaxi technology already proving itself in permissive states, New Jersey owners are being asked to ensure their state doesn’t lock out the future of transportation.
Turn-by-turn navigation is not new technology.
For over two decades, drivers have relied on Garmin, TomTom, and later smartphone apps like Google Maps and Waze to receive precise, reliable directions. These systems have guided millions safely through unfamiliar cities, highways, and backroads with remarkable effectiveness. They handle real-time traffic, construction detours, and complex intersections with minimal fuss.
Yet Tesla, the company that promised revolutionary Full Self-Driving (FSD), continues to struggle with this foundational capability. As FSD (Supervised) v14.3.4 has started rolling out to cars this week, navigation remains its glaring Achilles’ heel, undermining the entire autonomous vision.
Tesla Summon got insanely good in FSD v14.3.2 — Navigation? Not so much
Tesla’s FSD excels in many driving behaviors—smooth acceleration, confident lane changes in ideal conditions, and responsive handling of visible obstacles. However, when it comes to following a route accurately, the system falters repeatedly.
Owners report wrong turns, missed exits, inefficient routing through local roads instead of highways, phantom speed limit errors, and even directing vehicles to building rear entrances. Interventions for navigation issues often outnumber those for core driving maneuvers. Tesla has begun surveying owners specifically about these errors, acknowledging the problem after years of complaints.
Navigation is perhaps my biggest complaint when it comes to FSD, because sometimes, we do know better. Some of us have been living in our areas for our entire lives, but even those who have not have years or even decades of experience driving on local roads. We might know a little better about routing.
But the navigation mistakes are more than just FSD potentially taking a slightly different route that may or may not save you a few minutes. Sometimes, they’re genuinely mind-boggling.
This isn’t just annoying; it cascades into broader failures. A flawed route plan confuses the AI’s decision-making, leading to hesitant behavior, unnecessary disengagements, or dangerous maneuvers like attempting impossible U-turns or ignoring clear ramps. In a system meant to operate with minimal supervision, unreliable navigation erodes trust.
More often than not, false or plain incorrect navigation is what causes me to interrupt FSD operation. Unfortunately, I believe the latest FSD version is the worst example of it, and it leads me to believe that Tesla might be making some changes; they’ve just made them in the wrong direction.
It makes you wonder: Why is a company that has done so much with the progress of FSD and autonomy struggling so much with navigation, something that is not new and has been around a long time?
Multiple Data Sources
First, Tesla’s navigation relies on a fragile patchwork of multiple data sources—Google Maps, TomTom, OpenStreetMap, Valhalla, and its own fleet-derived data—stitched together rather than a single authoritative map. When these conflict on lane geometry, road status, or turn details, the system hesitates or chooses incorrectly.
Traditional GPS providers maintain centralized, regularly validated databases with professional curation and rapid updates. Tesla’s hybrid approach, while innovative in crowdsourcing, introduces inconsistencies that a purely vision-based or end-to-end AI approach may not easily reconcile in real time.
Persistent Learning
FSD seems to struggle with persistent learning from driver interventions.
Unlike consumer apps that quickly adapt to repeated corrections or user preferences (e.g., avoiding certain routes or remembering habitual detours), Tesla’s FSD often fails to internalize fixes on the same trip or across similar scenarios. Owners note making the same manual override multiple times without the routing engine updating its behavior meaningfully.
This stems from the neural architecture prioritizing real-time perception and control over long-term route memory and personalization, making navigation feel rigid and “opinionated” compared to the adaptive logic in Waze or Google Maps.
I noticed that when I asked Grok to try and get me home a certain way (a way that FSD routinely took in the past because it was the most efficient), it had to place a waypoint between my location at the time and my house. When I went to edit the waypoint out, as Grok had placed it for a way to get FSD to get off the highway at the right exit, it was stumped again, rerouted, and took a longer way home.
The next thing I’ve noticed, and this might be controversial, is that Nav has gotten even worse.
I think that might actually be a good thing; Tesla seems to be adjusting it. They just need to adjust it the opposite way.
The car is taking extremely strange routes to very… https://t.co/UHg3tVfNA2
— TESLARATI (@Teslarati) June 16, 2026
Reasoning, Scaling, and Intuition
Third, scaling navigation for unsupervised or robotaxi ambitions requires not just accuracy but adaptability and user-like reasoning. Current FSD often defaults to single routes that ignore driver preferences or real-world nuances like time-of-day traffic patterns. It fails to match the intuitive, context-aware planning that traditional systems have refined over the years.
Resolving navigation is critical for several reasons. Practically, it is the backbone of any autonomous journey: without trustworthy routing, the car cannot reliably reach destinations, rendering FSD useless for robotaxis or hands-free commutes. Safety depends on it—mismatched plans create hesitation in merges or intersections, increasing accident risk.
Economically, Tesla’s valuation and future hinge on FSD delivering unsupervised driving; persistent navigation flaws delay regulatory approval and erode consumer confidence. For owners who paid premiums for FSD, these issues represent unfulfilled promises. While it is unlikely Tesla will lose too many customers due to bad navigation, some will be frustrated with the constant need for human input.
Tesla has achieved miracles in electric vehicles and battery tech. Mastering turn-by-turn—technology Garmin nailed in the early 2000s—should not be this hard. By investing in tighter data integration, faster learning loops from interventions, and more intuitive routing algorithms, Tesla could close this gap.
Until then, FSD’s navigation struggles highlight a humbling truth: even the most ambitious innovator must sometimes master the basics before conquering the future.
A Tesla Cybertruck driver in the United Kingdom had their all-electric pickup seized by local police in the Greater Manchester area after the department cited “legitimate concerns.”
Last Thursday, police saw the pickup on the roads and decided to pull the driver over. Greater Manchester Police said:
“Whilst this may seem trivial to some, legitimate concerns exist around the safety of other road users or pedestrians if they were involved in a collision with the Cybertruck.”
🚨 A Tesla Cybertruck, which is illegal to drive in the UK due to safety concerns, has been seized by police in Greater Manchester
“Whilst this may seem trivial to some, legitimate concerns exist around the safety of other road users or pedestrians if they were involved in a… pic.twitter.com/cqhdPok3DM
— TESLARATI (@Teslarati) June 16, 2026
The Cybertruck in question was, according to the BBC, registered and insured abroad and was confiscated. The driver, who is a UK resident, was reported.
The Greater Manchester Police Department then added:
“The Tesla Cybertruck is not road-legal in the UK and does not hold a certificate of conformity.”
The Cybertruck cannot be legally driven in the UK because it has no UK Type Approval for operation in the country. This is due to some safety concerns, which are related to its angular shape and design. The stainless steel exoskeleton has sharp edges and projections that violate UK/EU rules on pedestrian protection.
Tesla has considered creating what it referred to as an “international version” that would be approved for operation in Europe. However, there has been no real movement on that front by the company, as it has been focused on the Robotaxi rollout primarily.
Tesla urges New Jersey owners to oppose new bill that could block Robotaxi
Tesla’s Navigation Nightmare: Why the easiest part of FSD might be the hardest
