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Ever wondered how engineers model ride comfort? This 2‑DOF quarter‑car simulation compares passive vs active suspension under road excitation — a classic problem in vehicle dynamics and control. From theory → simulation → insight 💡 🔗

65,205 次观看 • 3 个月前 •via X (Twitter)

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Model-Free Reinforcement Learning (MFRL) has been alluring, especially with supercharged compute with physics on GPU. However, the methods use 0-th order gradients, and are often not the best optimizers. Can we do better than PPO in continuous control for robotics? Turns out yes! 🥳 tl;dr: Faster, better RL than PPO in continuous control 💪 The answer lies in using more information from the simulation. We are juicing the simulation on GPU as it is, why not use it for gradients as well? This has been a driving question in a series of our works. We first studied this problem in ICLR 2022 paper on Short Horizon Actor Critic Naive gradient based methods are stuck in local minima and have exploding/vanishing gradients. SHAC solved this problem truncated rollouts and model based value estimation, where the model is Differentiable Sim. This boosted sample efficiency and wall-clock time immensely especially in high dimensional systems such as humanoids Yet, given enough compute PPO often caught up. Our follow up paper on on Adaptive Horizon Actor Critic at ICML 2024 discovers the cause and provides a fix. However, we find that even when given ground-truth dynamics, not all gradients are useful due to sample error. 1st-Order Model-Based Reinforcement Learning methods employing differentiable simulation provide gradients with reduced variance but are susceptible to bias in scenarios involving stiff dynamics, such as physical contact. We find that back-propagating through contact and long trajectories drastically reduces gradient accuracy. Using this insight, we propose AHAC to dynamically adapt its roll-out horizon to avoid differentiating through stiff contact. AHAC is a first-order model-based RL algorithm that learns high-dimensional tasks in minutes (wall clock) and outperforms PPO by 40%, even in the limit of data provided to PPO. This work is led by Ignat Georgiev alongside Krishnan Srinivasan, Jie Xu, Eric Heiden and ample assistance from warp team at NVIDIA Robotics (Miles Macklin)

Animesh Garg

52,308 次观看 • 2 年前

𝗣𝗼𝗽𝘂𝗹𝗮𝗿 𝗼𝗽𝗶𝗻𝗶𝗼𝗻: "𝗝𝘂𝘀𝘁 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝗺𝗼𝗿𝗲 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗱𝗮𝘁𝗮." After working with many 𝗿𝗼𝗯𝗼𝘁 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 teams who've fallen into the simulation trap, here's what I've learned: Simulation teaches your robot to be really, really good at simulation. Unlike blind locomotion policies that can get away with sim-to-real transfer because they rely mainly on proprioception and contact forces, 𝘃𝗶𝘀𝗶𝗼𝗻-𝗴𝘂𝗶𝗱𝗲𝗱 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗲𝘅𝘁𝗿𝗲𝗺𝗲𝗹𝘆 𝘀𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗲 𝘁𝗼 𝘃𝗶𝘀𝘂𝗮𝗹 𝗱𝗼𝗺𝗮𝗶𝗻 𝗴𝗮𝗽. The subtle differences accumulate: - Simulated friction vs real surface textures - Perfect lighting vs shadows, reflections, glare - Ideal object geometries vs manufacturing tolerances - Instantaneous sensor readings vs real-world noise and latency - Clean backgrounds vs cluttered, dynamic environments 𝗧𝗵𝗲 𝗰𝗹𝗮𝘀𝘀𝗶𝗰 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻: Week 1: "Our model works perfectly in sim!" Week 2: "Let's collect some real data to fine-tune." Week 3: "The real data completely contradicts what the sim taught..." Week 4: "Okay, let's collect way more real data." Month 2: "We basically need to retrain from scratch." 𝗧𝗵𝗲 𝗽𝗮𝗶𝗻𝗳𝘂𝗹 𝘁𝗿𝘂𝘁𝗵: There's no shortcut to real-world data collection for vision-based manipulation. Simulation is amazing for debugging, prototyping, safety testing, and of course to supplement your real data. But it's not a substitute for understanding how your robot actually behaves in the actual environment. 𝗪𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀: Use simulation strategically - for exploring edge cases, testing safety boundaries, and rapid iteration. But build your production models on real data from real environments. The teams that succeed treat simulation as a powerful tool, not a magic solution. This is why Neuracore focuses on making real-world data collection so much easier and faster. Because the physics of your actual environment can't be simulated away. 𝗪𝗼𝗿𝗹𝗱 𝗺𝗼𝗱𝗲𝗹𝘀, 𝘆𝗼𝘂 𝘀𝗮𝘆? 𝗪𝗲𝗹𝗹, 𝗽𝗲𝗿𝗵𝗮𝗽𝘀 𝗺𝗼𝗿𝗲 𝗼𝗻 𝘁𝗵𝗮𝘁 𝗶𝗻 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝗽𝗼𝘀𝘁! 𝗪𝗵𝗮𝘁'𝘀 𝗯𝗲𝗲𝗻 𝘆𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝘀𝗶𝗺-𝘁𝗼-𝗿𝗲𝗮𝗹 𝘁𝗿𝗮𝗻𝘀𝗳𝗲𝗿? 𝗛𝗮𝘀 𝗶𝘁 𝘄𝗼𝗿𝗸𝗲𝗱 𝗮𝘀 𝘄𝗲𝗹𝗹 𝗮𝘀 𝗲𝘅𝗽𝗲𝗰𝘁𝗲𝗱?

Stephen James

31,009 次观看 • 1 年前

Around 8:55 am on Boxing Day 26th December 2025, as I was coming back from my night duty, I saw this car doing like 80-90 miles/hr on a 60 mile/hr road. Temperature was 1 degree Celsius and roads were icy. I was worried because that road has loads of bends and it was crazy to see someone speeding like that on that road. Not up to 3 minutes, this chap approaching a bend suddenly went on his brakes, then lost control as the car started skidding. To our left is a hill, and beyond this hill is a river. This guy climbed that hill with speed. He drove on it for like 5 seconds while trying to re-establish control of the car, then the car came down and he struggled for another 3 seconds and then regained control, he sped off and continued driving. By this time, I had slowed down and was observing what was happening as I was just behind him. The broken parts of his car littered the road but this dude continued driving and speeding. This got me worried, he had a near death experience, every normal person would stop, park and soak in whatever that happened, and most especially, see his car and the damage done. This guy continued speeding like a mad man. I couldn’t call police because I was driving and not allowed to touch my phone, but I was dead worried this dude might be driving a stolen car ( because how do you smash your car in this economy and not park to even see the damage?). The tyre might be damaged and if he continues speeding like that, can have a tyre burst and crash into on coming vehicle. So I made up my mind to follow him. I started flashing lights, he noticed and slowed down, then I went close but he sped off again. I followed him again and continued flashing lights. I was just policing him until he parked and I double crossed him and had a go at him. I sha called the police and passed his details to him. So I’m posting the plate number in case someone stole your car, definitely must be this guy!! Contact police, they have the details and the area the incident took place.

Donpir

757,329 次观看 • 7 个月前

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

Axis Robotics

27,858 次观看 • 7 个月前

THE TESLA MODEL S: THE CAR THAT MADE ELECTRIC VEHICLES SERIOUS When the Model S launched in 2012, the entire world still saw EVs as slow, boring, short-range toys for tree-huggers. The Model S changed that narrative overnight. It wasn’t just an electric car — it was a statement. Here’s why the Model S was so important for EV adoption: • It proved EVs could be faster and better than gas cars 0–60 mph in under 4 seconds (later Plaid versions under 2 seconds) while being completely silent and smooth. It beat most supercars off the line and made “electric” synonymous with performance. • It delivered real long-range capability Over 300 miles of range when most EVs at the time struggled to reach 100 miles. Suddenly, road trips became possible and “range anxiety” started to feel outdated. • It introduced over-the-air updates The first production car that could get major performance upgrades, new features, and safety improvements wirelessly — like a smartphone on wheels. This changed how people think about car ownership forever. • It forced the entire auto industry to respond Legacy manufacturers who had been dragging their feet on EVs suddenly rushed to catch up. The Model S basically lit the fuse for the modern EV revolution. • It made luxury electric desirable Premium interior, massive touchscreen, ridiculous acceleration, and futuristic design turned EVs from “compromise” into “aspiration.” Without the Model S proving that electric cars could outperform and out-luxury gasoline vehicles, we wouldn’t have the Model 3/Y explosion, the Cybertruck, or the flood of competitors now racing to go electric. The Model S didn’t just sell cars. It changed the future of transportation. It took EVs from niche to mainstream and showed the world what was possible.

Tesla Owners Silicon Valley

11,056 次观看 • 4 个月前

Breaking news 🗞️ 🚨 Tesla just quietly solved a problem in Australia. The Model X is gone from our market. But the new Model Y L Premium AWD might actually be the closest thing we have to a replacement. And honestly… it makes a lot of sense. ⚡ Tesla Model Y L – Key Specs • 0–100 km/h: ~5.0 sec • Range: ~681 km WLTP • Top speed: 201 km/h • Seating: 6 adults • Supercharging: 250 kW • ~288 km added in 15 min 💰 Australian pricing (before on-road costs) Model Y Long Range AWD → $68,900 Model Y L Premium AWD → ~$74,900 So for roughly $6k more, you get: ✔ 3 rows ✔ 6 seats ✔ Much larger cabin ✔ ~400L extra cargo capacity ✔ Longer wheelbase ✔ Even more range 📊 Quick comparison Model Y Long Range AWD • 5 seats • ~600 km range • 0–100 km/h: 4.8 sec • $68,900 Model Y L Premium AWD • 6 seats • ~681 km range • 0–100 km/h: 5.0 sec • ~$74,900 So performance drops slightly, but practicality goes way up. 🇦🇺 Why this matters in Australia Since Tesla stopped selling the Model X locally, there has been a real gap in the lineup for larger families. The Model Y L doesn’t completely replace the X. You lose things like: ❌ Adaptive air suspension ❌ Driver instrument cluster ❌ Falcon Wing doors ❌ Some luxury interior touches But you still get: ✔ Tesla software ecosystem ✔ Supercharger network ✔ Massive range ✔ Practical 3-row seating And at a much lower price than a Model X ever was. 👨‍👩‍👧‍👦 Who this is perfect for • Growing families • Current Model Y owners needing more space • Former Model X buyers • Anyone considering EV9 / EX90 but wanting Tesla’s ecosystem Personally, as a Model X owner, this is the first Tesla sold in Australia that actually feels like a realistic successor. I’m seriously considering replacing my Model X with the Model Y L, possibly around the end of Q2 or mid-Q3 this year. Not a perfect Model X replacement. But for Australia right now? This might be Tesla’s smartest family vehicle yet. ⚡🇦🇺 ORDER NOW : Tesla Australia & New Zealand Tesla AI

Tesla in the Gong 🇦🇺🦘🤖🚕

21,519 次观看 • 5 个月前

The three-body problem is a classic and notoriously difficult question in physics and mathematics. It asks: How do three objects, such as stars, planets, or moons, move under the influence of each other’s gravity? Unlike the simpler two-body problem, which has precise and predictable analytical solutions (like the Earth orbiting the Sun in an ellipse), the three-body problem quickly becomes chaotic and unpredictable. This complexity arises because each object's motion constantly affects, and is affected by, the other two. These gravitational interactions form a tangled and unstable system. In fact, there's no general formula that can solve all three-body scenarios exactly. This was first demonstrated in the 19th century by Henri Poincaré, whose work laid the foundations for chaos theory. While exact solutions remain elusive, scientists have discovered certain special cases where the motion is stable or periodic. One well-known example is the Lagrange points, where three bodies can maintain a stable triangular configuration. However, such neat solutions are rare. Today, thanks to powerful computers, researchers can simulate three-body systems with remarkable accuracy, helping us study triple-star systems, exoplanets, and asteroid dynamics. Yet even small changes in the starting conditions can lead to dramatically different outcomes, highlighting the sensitive dependence on initial conditions that defines chaotic systems. The three-body problem is actually a specific case of the broader n-body problem, where n can be any number of interacting bodies. As n increases, the complexity and unpredictability rise even further. The three-body problem serves as a vivid example of how simple laws of nature, like Newton’s law of gravity, can produce behavior that is intricate, unexpected, and profoundly difficult to predict.

Erika 

215,611 次观看 • 1 年前

May this young man's Soul Rest in Peace. The carnage continues due to unprecedented levels of greed, dereliction of duty, and lack of accountability. The funds are actually there and have been provided over the past decade. The Ministry of Finance Ministry of Finance has released about UGX 100 Billion since 2019 for a Department of Government whose main responsibility and objective should be prevention of road crashes. These funds have been spent on activities with very minimal impact on road safety. This information has been shared with IGG, Office of the Auditor General Uganda, Criminal Investigations Directorate-UPF. How do you explained the death of a young man in a fatal road crash that caused the incineration of the vehicle which he was driving? He must have been driving in one of the best motor vehicles in this country given the wealth of his family. He was driving a motor vehicle which probably had good safety features on a smooth road without pot holes. It is easy to draw conclusions like he was speeding, he was fatigued, he lost control of his motor vehicle, he made errors, e.t.c. Whereas all that may be true, did he deserve to die in such a gruesome way for those errors in judgement? The answer is a BIG NO. The truth is government failed him in so many ways. At the center of modern day road safety management is recognition that errors made by road users (drivers, motorcyclists, cyclist and pedestrians) MUST NOT lead to their demise. Here is a situation he found himself in: Concrete Barriers on a road that encourages high speed. Who has the onus to remove this hazard? Government. I could have perished in the same circumstances a few years back before the completion of the KEE on the way to the Airport. It was a rainy early morning at about 5:00am and I drove through barriers which were made of plastic. So why were these not made of plastic? Was there a provision for lighting on that section? If not, it should have been there. This is what we mean when we say roads should be forgiving. Apart from their design, road sides are the major reference here. The road should have forgiven him for this error if it had plastic reflective barriers. These kind of barriers would have caught his attention and he would have stopped his car instead of killing him. Were the traffic lights functional? Why was there no hazard signage or warning of any sort since there were barriers in middle of the road? This section of the road is built as an expressway and as such encourages speeding especially at any time beyond midnight. There are no traffic calming measures in place yet it is freely used by pedestrians and Boda-Bodaz like any other city road. Roads are supposed to be designed and constructed bearing in mind the activities that take place around them (Land Use). Interventions to control speed must be implemented if there is a high volume of different road users. They would have got his attention and he would not have died. A glaring example of contractors that do not care about road safety is on the section of Kampala-Masaka in the swampy section of Lwera. The Concrete Barriers are there and in places where they are not, there is nothing preventing a motor vehicle falling off into the swamp. Not even a rope. UNTIL THE DAY WHEN THE LEADERSHIP OF THIS COUNTRY ACCEPTS THAT ROAD CRASHES ARE PREVENTABLE AND PARLIAMENT STARTS DEMANDING ACCOUNTABILTY ON ROAD SAFETY, THIS CARNAGE WILL CONTINUE. ACCEPTING THERE IS A PROBLEM IS THE FIRST STEP TO SOLVING IT OR MANAGING IT. TOMORROW IT MIGHT BE ME OR YOU. LETS STOP PAYING LIP SERVICE TO UGANDANS ESPECIALLY ALL THOSE FAMILIES THAT LOSE LOVED ONES IN THIS MESS OF GREED AND LACK OF ACCOUNTABILITY. Uganda Police Force Parliament of Uganda Parliamentary Forum on Road And Water Safety Road Safety Advocacy Coalition Uganda 🇺🇬 Road Sense Kenya - RSK Brian 4RoadSafety roadsafetypros Road Safety Awareness Initiative (Kenya) Road Safety Alliance UN Road Safety Anita Annet Among Safe Transport and Survivors Support Uganda Legacy Road Safety Initiative (LRSI) Uganda Professional Drivers' Network Percy B Mulamba Munwankyo CISCOT Civil Society Coalition On Transport UG Kampala Capital City Authority (KCCA) Greater Kampala Metropolitan Area - UDP Office of the Prime Minister - Uganda

Ronald Amanyire

41,895 次观看 • 1 年前