This is one of the more unusual flying robots I have seen.
Researchers at the University of Tokyo built DRAGON, a transformable aerial robot that can change its shape while flying.
Instead of a rigid frame, it is made of four linked segments.
Each segment has two ducted rotors mounted on gimbals, so every link can redirect thrust independently.
• The robot controls its full pose in SE(3) while reshaping mid-air
• It can form different geometries to stabilize itself or interact with objects
• The navigation stack computes the most efficient shape for the current task
• It lifts over 3 kg, which is unusually high for this class of system
This effectively turns a drone into a flying manipulator. Not a gripper bolted on top, but the airframe itself becomes the manipulator.
The authors also mention an interesting idea for range extension: letting the robot walk on the ground when flying is not required.
Paper
https://t.co/kVnw7mS62a
——-
Weekly robotics and AI insights.
Subscribe free: https://t.co/9Nm01QUcw3
¡Me he apuntado al desafío BetterMe Fitness de 28 días! 🧘♀️ Es hora de transformar mi cuerpo y mi mente. ¡Motivémonos mutuamente! #BetterMeWallPilatesCheck it out! ⬇️https://t.co/I1TYk3E9oS
This is the most fun moment to be a developer in years.
The AI tools are imperfect, the patterns are still emerging, and there's genuine room for experimentation. Roll up your sleeves and build something. The earthquake is further opening up what's possible.
The best news about this new layer: traditional engineering skills are more valuable than ever, not less. It helps us minimize shipping slop.
Developers who already invested in CI/CD, testing, documentation, and code review are having the most success with AI tools. These "boring" foundations are accelerators. They turn agents from chaos generators into productivity multipliers.
The real opportunity is learning to work at a different altitude. Instead of typing syntax, we're reviewing implementations, catching edge cases, and shipping features in hours that used to take days. That's genuinely exciting.
Yes, there's a learning curve. Understanding how to provide context, iterate on plans, and review AI-generated code quickly takes practice. But this is learnable through doing - build small tools, review everything, develop intuition through repetition.
The multiplier potential is real when you combine AI speed with engineering judgment. We're not replacing coding skills but we're finally able to focus them on the interesting problems while delegating the tedious parts.
Andrej Karpathy recently coined the term “vibe coding” to describe how LLMs are getting so good that devs can “give in to the vibes, embrace exponentials, and forget that the code even exists.”
In this episode of the @LightconePod, the hosts discuss this new way of programming and what it means for builders in the age of AI.