For 41 years, to find shortest paths in a... graph, the Dijkstra’s algorithm, was seen as the best possible way. Not any longer: Now a team from Tsinghua University has beaten it. They created the first faster algorithm for directed shortest paths since 1984. • Faster than Dijkstra on large sparse graphs • Works with real, non-negative edge weights • Proves that sorting is not the main bottleneck anymore Shortest-path algorithms power maps, GPS, logistics, networking, and robotics. This result shows that even the most “finished” algorithms can still be improved. Thanks for sharing, Md Ismail Šojal 🕷️! 📍 Paper: —— Weekly robotics and AI insights. Subscribe free:show more

dog follows pointer + avoids collisions with no dependencies... - builds the page as a graph, more subdivisions towards the text for perf - dijkstra shortest pathshow more

Dijkstra’s algorithm doesn’t just tell you the fastest route... to one destination. Instead, it gives you an ordered list of travel times from your current location to every other point that you might want to visit — a solution to what researchers call the single-source shortest-paths problem.show more

Oldies but goldies: L.E. Dubins, On Curves of Minimal... Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents, 1957. Dubins paths are the shortest path for cars that can only move forward.show more

A simple MPC controller for path tracking implemented in... python. Few resources actually teach it without assuming a PhD first. This open-source project does exactly that: An iterative MPC tracking controller (built with CVXPY and MuJoCo) that shows how to make a robot (or autonomous vehicle) follow a path and avoid obstacles in real time. → Autonomous logistics, robot navigation, vehicle control; there's a path-following algorithm behind all of it. → Most implementations are black boxes. This one shows you what's actually happening. → It uses convex optimization (no non-linear solver), and still handles real vehicle physics through iterative linearization. What makes it practical is that you can run it with the mushr car model in MuJoCo, or headless without any physics simulation if you just want to understand the logic. If you're making the jump from classical control to real-time optimisation, this is a very good starting point. 📌 Repository: ——- Weekly robotics and AI insights. Subscribe free:show more

🚀 Imitating shortest paths in simulation enables effective navigation... and manipulation in the real world. Our findings fly in the face of conventional wisdom! This is a big joint effort from PRIOR Ai2 (6 first authors!).show more

Almost every robot you see... runs on this equation.... Not AI. Not machine learning. PID. For over 100 years, this simple control algorithm has been quietly keeping robots balanced, drones stable, industrial machines precise, and even rockets on course. Most people never hear about it. Yet without PID, many of today's robots wouldn't even stand upright. If you could only learn ONE control algorithm in robotics... this would be it. What's the most impressive application of PID you've seen? 🎥 Media: medcorreia ( Instagram ) ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technology shown. Credit belongs to the original creators.show more

Its not every day you wake up to find... that the Pope has made your lifes work the central focus of his papacy: “Disarming AI means freeing it from the mentality of “armed” competition [..] This entails a race for ever more powerful algorithms and larger datasets, driven by the desire to secure geopolitical or commercial dominance." - POPE LEO XIV, May 2026 Here, “disarmed” means “neutralised” in the sense that this should not be a differentiator The Innovation Game (TIG) was created to keep data and algorithms open, in order to prevent monopolistic control It's not just an aspiration, it's an economic mechanism that makes open data and open algorithms the rational economic choice • All algorithms are published openly by TIG • If you are willing to make the data you process with an algorithm open, you can use it free-of-charge • Alternatively, if you would like to keep this data private, there is a fee to pay for using the algorithm • All fees are used to fund more open innovation Its an elegant, global, self-reinforcing engine The logical end point of monopoly is that innovation stops We cannot allow that to happen Pope Leo XIV I would be grateful for your thoughts on The Innovation Gameshow more

Two months ago, at an RSS workshop panel, I... said that general real2sim reconstruction is still far from solved, especially for articulated objects, which will be a major bottleneck for robotics. But now that has totally changed with Astra; using several photos as input, we can easily reconstruct vivid articulated scenes. The result is so impressive! Excited to see how it will shift the paradigm of real2sim2real and agentic learning for robotics and humanoids.show more

AI in robotics gets all the attention right now,... but sometimes the most interesting work is very practical. Viet built a small vision system that counts potatoes on a conveyor belt. No giant dataset. No huge model. Just a clear problem and a smart setup. He used Ultralytics’ ObjectCounter, trained a tiny YOLO11 nano model, and because there was no potato dataset, he annotated a single frame with SAM 2 and trained from that. One frame. Still works across the whole video. It is a good reminder that useful AI in industry often looks like this. Focused. Lightweight. Solves a real task. If you work in manufacturing or robotics, these small systems are usually the fastest wins. They save time, reduce errors, and do not need massive infrastructure. Nice work, Viet. His projects: —- Weekly robotics and AI insights. Subscribe free:show more

Multi-axis 3D printing with curved layers! 🖨️ Researchers from... the The University of Manchester introduced a neural network-based computational pipeline as a representation-agnostic slicer for multi-axis 3D printing. Traditional 3D printing works like stacking pancakes, flat layers on top of each other. 🥞 This often requires temporary support structures that get thrown away after printing, wastes material, and creates weaker parts. Multi-axis 3D printing can print along curved paths that follow the object's natural shape. This eliminates support structures and makes stronger parts. But figuring out these curved paths is mathematically complex, you need to avoid collisions, respect what the printer can physically do, and optimize for strength. The neural network solves this automatically. It learns to create a "field" around the object, then extracts curved printing paths from this field. Because the entire process is differentiable (translation for non-math specialists, meaning you can optimize it end-to-end), the AI can directly optimize for manufacturing goals like "no support structures needed" and "make it as strong as possible." Here's the project: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →show more

In Paralives, you can set Parafolks with different heights,... with 1.96m (6'4") being the tallest and 1.49m (4'10") being the shortest for adult. Here's what it looks like when the shortest adult kisses with the tallest 🤯show more

Dear Andrej Karpathy, Update on this. Earlier this month... The Innovation Game Foundation announced a new SOTA routing algorithm had been collaboratively developed and submitted to The Innovation Game. The algorithm demonstrated the largest single perfomance jump in the modern history of the field on standard academic benchmarks: This success is a powerful proof of concept. I believe the wider implications will also interest you. As you know, the "Source" in AI is algorithms and data. These algorithms are typically for "hard to solve but easy to verify" problems. Remarkably, this allows the creation of a market for pricing improvements to these algorithms (roughly, the market is created by "racing" the algorithms, to see which can produce proof-of-work fastest). Availability of a market mechanism means open development of the algorithms can be funded by capturing a portion of the value they generate, and allocating it back to algorithm developers. The allocation is efficient, naturally integrating information (such as hardware availability) through revealed preferences. Importantly, market allocation is also "impersonal", which mitigates the risk to community cohesion that has historically afflicted Open Source projects offering monetary reward. Note: That a market for pricing code could extend Open Source to areas requiring monetary reward was (as far as I know) first suggested by Eric Raymond in 1999 Eric S. Raymond : Conclusion: The structure of Open Source AI means it can operate commercially. For example, value captured via Open Source "dual licensing", with allocation of the value by a market generated by proof-of-work. I'd love to hear your thoughts on this. Please see for more detail.show more

Elon Musk on the scale of what AI +... robotics are about to unleash: “AI and robotics is a supersonic tsunami. This is really gonna be the most radical change that we’ve ever seen”show more

Most imitation learning policies break when the camera moves... or the robot changes. NOT THIS ONE 👇 [📍 Bookmark for later ] A new 3D scene representation encoder, tackles this by enabling zero-shot generalization to unseen embodiments and viewpoints… And it works with any IL algorithm. The trick? •Use a 2D foundation model to extract semantic features •Lift them into 3D space for localization (not semantics) •Condition the IL policy on this spatially grounded vector Across 93 simulated and 6 real tasks, Adapt3R: ✅ Maintains IL performance on LIBERO & MimicGen benchmarks ✅ Outperforms DP3 and 3D Diffuser Actor in most settings ✅ Holds >80% success on LIBERO even with large camera rotations Thanks for sharing this, Animesh Garg & Albert Wilcox! 📍Paper: Website: Code:show more

NEW CHALLENGE ANNOUNCEMENT Announcing the Energy Arbitrage designed in... collaboration with CryptoEconLab It's been on testnet for over a month and live on mainnet next week! So what is energy arbitrage and why does it matter? AI is eating electricity faster than grids can supply it The algorithms that decide how grid-scale batteries charge and discharge are becoming some of the most consequential algorithms on Earth, and until today they've been locked behind closed doors.show more

JUST IN: Reimagine Robotics has just emerged from stealth!... 🥷🏻 Its approach to robot training is one of the most human-centric I've seen. The founder is Jonathan Scholz, the person who built and led Google DeepMind's Applied Robotics team in London for seven years. This is not a first-time founder taking a swing at robotics. He has spent a decade at the frontier of the field. The philosophy is powerful. He calls it "monkey-see, monkey-do." 🐒 A worker shows the robot what to do. Watches it attempt the task. Corrects it on the spot. The robot learns. No specialist programmers. No months of integration. And it's already working in the real world: → A made-to-order plastics business trained robots to tend 3D printers overnight, removing print beds, operating latches, pressing controls → A hard drive disassembly facility built a three-robot cell combining robots and people to recover critical materials → Time to prototype and test a new robot behaviour reduced from one day to 10 MINUTES That last number is the one that changes everything. When testing a new behaviour takes 10 minutes instead of a day, the entire pace of deployment transforms. Scholz's framing of the human-robot relationship is worth reading carefully: "A robot that learns on the job depends on people. The worker identifies the bottleneck, shows the robot how to help, and corrects it until it is useful." It's August, and we keep getting robotics bangers week in week. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →show more