News
SpaceX will host Hyperloop Pod Competition next week, Jan 27-29, 2017
Get ready to see Hyperloop concept pods fire through the 1-mile test track located outside of SpaceX and Tesla’s Design Studio in Hawthorne, California, next week between January 27-29. Elon Musk and SpaceX first unveiled the idea for a new high-speed ground transport system called the Hyperloop on August 12, 2013 with the publication of a white paper, the Hyperloop Alpha Preliminary Design Study. SpaceX’s sponsored Hyperloop Pod Competition is an incentive prize competition created to inspire university students and independent engineering teams to design and build a subscale prototype transport vehicle (a “Hyperloop pod”) that will demonstrate technical feasibility of various aspects of the high speed transportation concept. To support this competition, SpaceX has constructed a test track outside of its headquarters which we had the opportunity to see during early construction last year.
There are three judging phases in the Hyperloop Pod competition: a design competition that was held in January 2016 and an on-track competition to be held January 27–29, 2017 (Competition Weekend I), followed by a Summer 2017 (Competition Weekend II). The original specification for the Competition Basic for the Design Weekend and the competition Weekend I, though no longer available at SpaceX, can still be found online.
DESIGN WEEKEND
The Design weekend was held in January 2016 at Texas A&M University. Awards were given in three categories:
SUBSYSTEM
Best Overall Subsystem Award: Auburn University | Auburn University Hyperloop Team.
DESIGN ONLY
Top Design Concept Award: Universitat Politècnica de Valencia | Makers UPV Team
DESIGN AND BUILD CATEGORY OVERALL
Massachusetts Institute of Technology | MIT Hyperloop Team
MIT Hyperloop Team’s design was awarded the “Best Overall Design Award”, among the 23 designs selected to move to the prototype stage. The design proposes a 250 kg (551 lb) pod with a carbon fiber and polycarbonate sheet exterior. It is elevated by a passive magnetic levitation system comprising 20 neodymium magnets that will maintain a 15 mm (0.6 in) distance above the track. The team says with air pressure at 140 Pascals, the pod could accelerate at 2.4 G and have 2 Newton aerodynamic drag when traveling at 110 m/s. The design includes a fail-safe braking system that automatically halts the pod should the actuators or computers fail, and low speed emergency drive wheels that can move the pod 1 m/s. Delft Hyperloop received a “Pod Innovation Award”, while Badgerloop at University of Wisconsin, Madison, Hyperloop at Virginia Tech, and HyperXite at UC Irvine each received a “Pod Technical Excellence Award.” The full list of Awards and news clips from the Design Weekend can be found at the Texas A&M University Engineering web site. Besides the winning teams, several other teams were invited to compete in the upcoming Competition Weekend I from the Design and Build category:
- rLoop (Non-student team)
- University of Waterloo | uWaterloo Hyperloop
- University of Washington | UWashington Hyperloop
- University of Toronto | University of Toronto
- University of Maryland and Rutgers University | RUMD Loop
- University of Florida | GatorLoop
- University of of Colorado, Denver | Team HyperLynx
- University of Cincinnati | Hyperloop UC
- University of California, Santa Barbara | UCSB Hyperloop
- University of California, Berkeley | bLoop
- Texas A&M University | TAMU Aerospace Hyperloop
- Technical University of Munich | WARR Hyperloop
- Purdue University | Purdue Hyperloop Design Team
- Oral Roberts University | Codex
- Lehigh University | Lehigh Hyperloop
- Keio University | Keio Alpha
- Drexel University | Drexel Hyperloop
- Carnegie Mellon University | Carnegie Mellon Hyperloop
In February 3, 2016 eight more teams advanced to Competition Weekend I.
- Cornell University + Harvey Mudd College + University of Michigan + Northeastern University + Memorial University of Newfoundland(Canada) + Princeton University | OpenLoop
- Louisiana State University | Bayou Bengals
- New York University | NYU Hyperloop
- RMIT University | VicHyper
- John’s High School | HyperLift
- University of Illinois at Urbana-Champaign | Illini Hyperloop
- University of Southern California | USC Hyperloop
- University of Wisconsin, Milwaukee | Mercury Three
In the end, 30 of the 115 teams that submitted designs in January 2016 were selected to build hardware to compete in Competition Weekend I. There were more than 1,000 applicants at earlier stages of the competition.
JUDGING CRITERIA
Originally, the second Phase of the competition was supposed to involve competitive runs in the Hyperloop test track to be awarded based on various classes (fully functional pod, susbsystem test pod, etc.) and pod mass. This phase of the competition was renamed“Competition Weekend I,” when SpaceX added a third phase of the competition, Competition Weekend II. The original SpaceX Hyperloop Pod Competition – Rules and Requirements for Weekend I can be seen at the end of this article. We’ve embedded a copy of the original document from SpaceX.
The Judging Criteria are listed in the document, and involve scoring in 4 different categories, for a maximum overall total of 2500 points.
- Category 1: Final Design and Construction (500 points)
- Category 2: Safety and Reliability (500 points)
- Category 3: Performance in Operations (500 points)
- Category 4: Performance in Flight (1000 points)
HYPERLOOP TEST TRACK
AECOM, a company that has designed and built some of the world’s most impressive transportation systems, was selected to design and build the world’s first Hyperloop test track as part of the pod competition hosted by SpaceX
The track is a straight one-mile run on Jack Northrop Avenue, between Crenshaw Blvd. and Prairie Ave. The SpaceX Hyperloop test track — or Hypertube — was designed in 2015 and was constructed in the fall 2016, reaching its full length of one mile by October 2016. The test track’s six-foot diameter steel tube includes a non-magnetic sub-track and said to be capable of achieving 99.8 percent vacuum. The test track itself is also a prototype, where SpaceX anticipates learning from the design, build process and evaluates how to apply automated construction techniques to future Hyperloop tracks.
The Hypertube test track is designed to enable competitors who implement a wide array of designs and build pods that will test a variety of subsystem technologies that are important to new vehicle transport systems. This will include Hyperloop-specific pods—with air-bearing suspension and low-pressure compressor designs—as well as wheeled vehicle and magnetic levitation rail designs that will support a wide array of vehicle technologies to be tested. While the Design Weekend held at Texas A&M University was open to the public, it is unclear if the Competition Weekend I will be as well, or if it will be an invitation only event like many of the SpaceX and Tesla events. Several inquiries for tickets posted to the Twitter account of the Hyperloop Pod Competition went unanswered. The Official SpaceX Hyperloop Pod Competition page does not shed any light on who will be able to attend either.
HYPERLOOP POD COMPETITION II
According to SpaceX, “based on the high-quality submissions and overwhelming enthusiasm surrounding the competition, SpaceX is moving forward with a second installment of the competition: Hyperloop Pod Competition II, which will culminate in a second competition in Summer 2017 at SpaceX’s Hyperloop test track. Hyperloop Competition II will be focused on a single criterion: maximum speed. The second competition is open to new student teams interested in competing on the test track, as well as to existing student teams who have already built and tested Pods to further refine their designs.” The Competition Weekend II event will be held in the Summer 2017 at the same SpaceX Hyperloop test track.
[pdf-embedder url=”http://www.teslarati.com/wp-content/uploads/2017/01/spacex-hyperloop-competition-rules.pdf”]
Elon Musk
Tesla Optimus V3 hand and arm details revealed in new patents
Two new patents, which were coincidentally filed on the same day as the “We, Robot” event back in October 2024, protect Tesla’s mechanically actuated, tendon-driven architecture.
Tesla is planning to soon reveal its latest and greatest version of the Optimus humanoid robot, and a series of new patents for the hands and arms, with the former being, admittedly, one of the most challenging parts of developing the project.
Two new patents, which were coincidentally filed on the same day as the “We, Robot” event back in October 2024, protect Tesla’s mechanically actuated, tendon-driven architecture.
The designs relocate heavy actuators to the forearm, route cables through a sophisticated wrist design, and employ innovative joint assemblies to achieve human-like dexterity while enabling lightweight construction and high-volume manufacturing.
Core Tendon-Driven Hand Architecture
The primary patent, which is titled “Mechanically Actuated Robotic Hand,” details a cable/tendon-driven system.
Actuators are positioned in the forearm rather than the hand. Each finger features four degrees of freedom (DoF), while the wrist adds two more.
Tesla’s Optimus V3 robot hand looks to have been revealed in a new international patent published today.
The patent describes a tendon/cable-driven hand:
• Actuators in the forearm
• Each finger has 4 degrees of freedom
• The wrist has 2 degrees of freedom
• Tendon-driven… pic.twitter.com/eE8xLEYSrx— Sawyer Merritt (@SawyerMerritt) April 16, 2026
Three thin, flexible control cables (tendons) per finger extend from the forearm actuators, pass through the wrist, and connect to the finger segments. Integrated channels within the finger phalanges guide these cables selectively—routing behind some joints and forward of others—to enable independent bending without unintended motion.
Patent diagrams illustrate thick cable bundles emerging from the wrist into the palm and fingers, with labeled pivots and routing guides. This setup closely mirrors human forearm-muscle and tendon anatomy, where most hand control originates proximally.
Advanced Wrist Routing Innovation
One of the standout features is the wrist’s cable transition mechanism. Cables shift from a lateral stack on the forearm side to a vertical stack on the hand side through a specialized transition zone.
Boom! @Tesla_Optimus 의 3세대 구조로 추정되는, 로봇 팔 및 관절에 대한 특허가 공개되었습니다.
아티클 작업에 들어가겠습니다.
1년 넘게 기다려 온, 정말 귀한 특허인데, 조회수 100만대로 터져줬으면 좋겠네요. 😉@herbertong @SawyerMerritt@GoingBallistic5 @TheHumanoidHub pic.twitter.com/CCEiIlMFSX
— SETI Park (@seti_park) April 16, 2026
This geometry significantly reduces cable stretch, torque, friction, and crosstalk during combined yaw and pitch wrist movements — common failure points in simpler tendon systems that cause imprecise or jerky motion.
By minimizing these issues, the design supports smoother, more reliable multi-axis wrist operation, essential for complex real-world tasks.
Companion Patents on Appendage and Joint Design
Two supporting patents provide additional depth. “Robotic Appendage” covers the overall forearm-to-palm-to-finger assembly, with a palm body movably coupled to the forearm and finger phalanges linked by tensile cables returning to forearm actuators. Tensioning these cables repositions the phalanges precisely.
“Joint Assembly for Robotic Appendage” describes curved contact surfaces on mating structures paired with a composite flexible member. This allows smooth pivoting while maintaining consistent tension, enhancing durability, and simplifying assembly for mass production.
Executive Insights on Hand Development Challenges
Tesla executives have consistently described the hand as the most difficult component of Optimus.
Elon Musk has called it “the majority of the engineering difficulty of the entire robot,” emphasizing that human hands possess roughly 27–28 DoF with an intricate tendon network powered largely by forearm muscles. He has likened the challenge to something “harder than Cybertruck or Model X… somewhere between Model X and Starship.”
In mid-2025, Musk acknowledged that Tesla was “struggling” to finalize the hand and forearm design. By early 2026, he stated that the company had overcome the “hardest” problems, including human-level manual dexterity, real-world AI integration, and volume production scalability.
He estimated the electromechanical hand represents about 60 percent of the overall Optimus challenge, compounded by the lack of an existing supply chain for such precision components.
These patents directly tackle the acknowledged pain points: relocating actuators reduces hand mass and inertia for better speed and efficiency; advanced wrist routing and joint geometry address friction and crosstalk; and simplified, stackable parts visible in the diagrams indicate readiness for high-volume manufacturing.
Implications for Optimus Production and Leadership
Collectively, the patents portray the Optimus v3 hand not as a mere prototype, but as a production-oriented system engineered from first principles.
The 22-DoF architecture, forearm-driven tendons, and crosstalk-minimizing wrist deliver a clear competitive edge in dexterity. They align with Musk’s view that high-volume manufacturing is one of the three critical elements missing from most other humanoid projects.
For Optimus to become the most capable humanoid robot, its hand needed to replicate the useful and applicable design of the human counterpart.
These filings demonstrate that Tesla has transformed years of engineering challenges into patented, elegant solutions — positioning the company strongly in the race toward general-purpose robotics.
News
Tesla intertwines FSD with in-house Insurance for attractive incentive
Every mile logged under FSD now carries a documented financial value—lower risk, lower cost—based on Tesla’s internal driving data rather than external crash statistics alone.
Tesla intertwined its Full Self-Driving (Supervised) suite with its in-house Insurance initiative in an effort to offer an attractive incentive to drivers.
Tesla announced that its new Safety Score 3.0 will automatically have a perfect score of 100 with every mile driven with Full Self-Driving (Supervised) enabled.
The change is designed to boost customers’ average safety scores and deliver noticeably lower monthly premiums.
The move marks the clearest link yet between Tesla’s autonomous driving technology and its proprietary insurance product. Tesla Insurance already relies on real-time vehicle data—such as acceleration, braking, following distance, and speed—to calculate a Safety Score between 0 and 100. Higher scores have long translated into cheaper rates.
Under the previous system, however, even brief manual interventions could drag down the average, frustrating owners who rely heavily on FSD. Version 3.0 eliminates that penalty for supervised autonomous miles, effectively treating FSD-driven segments as the safest possible driving behavior.
The incentive is immediate and financial. Drivers who keep FSD engaged for the majority of their trips will see their overall score rise, potentially shaving hundreds of dollars off annual premiums.
Tesla framed the update as a direct response to customer feedback, many of whom had complained that the old scoring model punished the very behavior it was meant to encourage.
For now, the program applies only to new policies in six states: Indiana, Tennessee, Texas, Arizona, Virginia, and Illinois.
Existing policyholders are not yet included, a point that drew swift questions from the Tesla community. Many owners in other states, including California and Georgia, expressed hope that the benefit would expand nationwide soon.
The announcement arrives as Tesla continues to roll out FSD Supervised updates and push for regulatory approval of more advanced autonomy. By tying insurance savings directly to FSD usage, the company is putting its own actuarial weight behind the technology’s safety claims.
Every mile logged under FSD now carries a documented financial value—lower risk, lower cost—based on Tesla’s internal driving data rather than external crash statistics alone.
Tesla has not disclosed exact premium reductions or the full rollout timeline beyond the six launch states.
Still, the message is clear: the more drivers trust FSD Supervised, the more Tesla Insurance will reward them. In an era when legacy insurers remain cautious about autonomous tech, Tesla is betting that its own data will prove the safest miles are the ones driven hands-free.
Elon Musk
Tesla finalizes AI5 chip design, Elon Musk makes bold claim on capability
The Tesla CEO’s words mark a strategic shift. Tesla has long emphasized software-hardware co-design, squeezing maximum performance from every transistor. Musk previously described AI5 as optimized for edge inference in both Robotaxi and Optimus.
Tesla has finalized its chip design for AI5, as Elon Musk confirmed today that the new chip has reached the tape-out stage, the final step before mass production.
But in a brief reply on X, Musk clarified Tesla’s AI hardware roadmap, essentially confirming that the new chip will not be utilized for being “enough to achieve much better than human safety for FSD.”
He said that AI4 is enough to do that.
Instead, the AI5 chip will be focused on Tesla’s big-time projects for the future: Optimus and supercomputer clusters.
Musk thanked TSMC and Samsung for production support, noting that AI5 could become “one of the most produced AI chips ever.” Yet, the key pivot came in his direct answer: vehicles no longer need the bleeding-edge silicon.
And thank you to @TaiwanSemi_TSC and @Samsung for your support in bringing this chip to production! It will be one of most produced AI chips ever.
— Elon Musk (@elonmusk) April 15, 2026
Existing AI4 hardware, which is already deployed in hundreds of thousands of HW4-equipped Teslas, delivers safety metrics superior to human drivers for Full Self-Driving. AI5 will instead accelerate Optimus robot development and massive Dojo-style training clusters.
The Tesla CEO’s words mark a strategic shift. Tesla has long emphasized software-hardware co-design, squeezing maximum performance from every transistor. Musk previously described AI5 as optimized for edge inference in both Robotaxi and Optimus.
Now, with AI4 proving sufficient, the company avoids costly retrofits across its fleet while redirecting next-generation compute toward higher-value applications: dexterous robots and exponential training scale.
But is it reasonable to assume AI4 enables unsupervised self-driving? Yes, but with important caveats.
On the hardware side, the claim is credible. Tesla’s FSD stack runs end-to-end neural networks trained on billions of miles of real-world data. Internal safety data reportedly shows AI4-equipped vehicles already outperforming average human drivers by a significant margin in controlled metrics (collision avoidance, reaction time, edge-case handling).
Dual-redundant AI4 chips provide ample headroom for the driving task, leaving bandwidth for future model improvements without new silicon. Musk’s assertion aligns with Tesla’s pattern of over-provisioning compute early, then optimizing ruthlessly, exactly as HW3 once sufficed before HW4 scaled further.
Optimus and our supercomputer clusters.
AI4 is enough to achieve much better than human safety for FSD.
— Elon Musk (@elonmusk) April 15, 2026
Unsupervised autonomy, meaning Level 4 or higher, is not solely a compute problem. Regulatory approval remains the primary gate.
Even if AI4 achieves “much better than human” safety statistically, agencies like the NHTSA demand exhaustive validation, liability frameworks, and public trust.
Tesla’s supervised FSD has shown rapid gains in recent versions, yet real-world edge cases, like construction zones, emergency vehicles, and adverse weather, still require driver intervention in many jurisdictions. Competitors like Waymo operate limited unsupervised fleets, but only in geofenced areas with extensive mapping. Tesla’s vision-only, fleet-scale approach is more ambitious—and harder to certify globally.
In short, Musk’s post is both pragmatic and bullish. AI4 is likely capable of unsupervised FSD from a technical standpoint. Whether regulators and consumers agree, and how quickly, will determine if Tesla’s bet pays off.
The company’s capital-efficient path keeps existing cars relevant while pouring future compute into robots. If the safety data holds, unsupervised autonomy could arrive sooner than many expect.

