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(1/6) X-Humanoid 🤖: Scaling up data for Humanoid Robots. We convert human daily activity videos (from Ego-Exo4D) into humanoid videos (i.e., Tesla Optimus) performing tasks like cooking or fixing a bike. This data can be potentially used to train robot policies and world models. 🔥 Project page: Paper link:

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Mike Shou

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87,999 Aufrufe • vor 7 Monaten •via X (Twitter)

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𝗥𝗼𝗯𝗼𝘁𝘀 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗺𝗼𝗿𝗲 𝗱𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻𝘀. 𝗧𝗵𝗲𝘆 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗳𝗮𝗶𝗹𝘂𝗿𝗲 — 𝗮𝗳𝘁𝗲𝗿 𝘄𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻𝘀. Most robot learning systems assume failure is the end of learning. In our new work, we study whether robots can improve after deployment by learning from their own failures, without any human intervention, teleoperation, or corrective labels. The key idea is simple: human videos contain structure about how the world works. We use them to learn cross-embodiment representations of action, dynamics, and value, enabling a shared predictive space between human behavior and robot experience. This allows a new learning loop: 👉 pretrain on human videos 👉 deploy robot policy 👉 observe failures 👉 reinterpret failures using human priors 👉 improve autonomously We evaluate this across 7 real-world manipulation tasks, showing: 📈 40% → 81% success rate 🏆 Strong improvements over π0.6 RECAP and RISE ✔️ Zero human intervention during post-deployment improvement 🧬 Generalizes across robot embodiments and policy backbones A key finding is that explicit failure repair significantly outperforms failure reweighting, yielding substantially larger gains under identical data conditions (+25 pts vs +5 pts on the same π0.5 base policy). Overall, the results suggest a shift in how we think about robot learning: Human videos are not only for pretraining policies. They can provide the structure needed for continual self-improvement after deployment. 📄 Paper: 🌐 Project: I am grateful for working with the fantastic leads Hanzhi Chen and Anran Zhang, and our collaborators Simon Schaefer, Kejia Chen, Shi Chen, Daniel Cremers. Special thanks to Stefan Leutenegger for co-advising this project with me. ETH Zürich TU München Microsoft Check out Hanzhi's 🧵 for more details

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Genesis AI just unveiled Eno. It's humanoid robot that challenges everything the industry assumed about what robots should look like. Forbes just called it 'the iPhone moment for humanoid robots'. No head. No face. No exposed motors or cables. 22 degrees of freedom per hand with different finger lengths (like actual human hands). Back-drivable for safety. Onboard cameras and tactile sensors. In demos: bundling wires with tape (genuinely hard, tape is sticky and unpredictable), performing lab automation with millimeter precision on unmodified equipment. Optional chest screen shows the robot's reasoning before it acts, a visual window into its mind to build trust. Powered by Genesis AI GENE foundation model. Payload 3-5kg per arm, 4-6 hours battery. Industrial deployments late 2026, homes much later. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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29,127 Aufrufe • vor 1 Monat

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Tesla Owners Silicon Valley

24,645 Aufrufe • vor 24 Tagen

Robots struggle with strict action rules…memory and symbols help them learn fast. [Project + Full video link ⬇️] Robots struggle when tasks require specific steps in a fixed order. What if memory helped them think symbolically and learn faster? Solving tasks like unlocking a door then opening it is hard for deep RL. But by learning constraint relationships and storing them in memory, robots can solve these tasks much faster; with fewer trials and less training. Why it works ✅ Learns symbolic rules about action constraints ✅ Uses memory to transfer what it learned across tasks ✅ Handles real-world exploration with just 30 minutes of data ✅ Needs 10x fewer episodes than deep RL approaches This memory-based method shows a promising path forward for robots learning structured, real-world tasks. Full video: Paper: Thank you, Mrinal Verghese for sharing this amazing work! 🙏
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Robots struggle with strict action rules…memory and symbols help them learn fast. [Project + Full video link ⬇️] Robots struggle when tasks require specific steps in a fixed order. What if memory helped them think symbolically and learn faster? Solving tasks like unlocking a door then opening it is hard for deep RL. But by learning constraint relationships and storing them in memory, robots can solve these tasks much faster; with fewer trials and less training. Why it works ✅ Learns symbolic rules about action constraints ✅ Uses memory to transfer what it learned across tasks ✅ Handles real-world exploration with just 30 minutes of data ✅ Needs 10x fewer episodes than deep RL approaches This memory-based method shows a promising path forward for robots learning structured, real-world tasks. Full video: Paper: Thank you, Mrinal Verghese for sharing this amazing work! 🙏

Ilir Aliu - eu/acc

10,241 Aufrufe • vor 1 Jahr

🚀 Optimus is gearing up for MASS PRODUCTION — and it’s about to change everything. Tesla is preparing its first large-scale Optimus factory in Fremont (replacing Model S/X lines) targeting 1 million robots per year, with a second-gen line in Austin aiming for 10 million annual capacity! Gen 3 is already walking, refining its moves, and the AI brains (powered by the same tech as FSD/Robotaxi) are accelerating fast. This isn’t just a robot — it’s Tesla’s next leap toward solving labor shortages, boosting productivity, and accelerating our sustainable energy + autonomy future. Imagine helpful Optimus bots in homes, factories, and beyond, freeing humans for higher pursuits while advancing the mission. The robotics revolution is closer than ever. 🤖✨ What excites you most about Optimus — household helper, factory teammate, or something bigger? Drop your vision below! 👇 Like if you’re bullish on Tesla’s multi-product future | Repost to spread the hype | Follow for daily Tesla energy 🔥 #Tesla #Optimus #RobotRevolution #FSD #SustainableFuture #Cybertruck
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🚀 Optimus is gearing up for MASS PRODUCTION — and it’s about to change everything. Tesla is preparing its first large-scale Optimus factory in Fremont (replacing Model S/X lines) targeting 1 million robots per year, with a second-gen line in Austin aiming for 10 million annual capacity! Gen 3 is already walking, refining its moves, and the AI brains (powered by the same tech as FSD/Robotaxi) are accelerating fast. This isn’t just a robot — it’s Tesla’s next leap toward solving labor shortages, boosting productivity, and accelerating our sustainable energy + autonomy future. Imagine helpful Optimus bots in homes, factories, and beyond, freeing humans for higher pursuits while advancing the mission. The robotics revolution is closer than ever. 🤖✨ What excites you most about Optimus — household helper, factory teammate, or something bigger? Drop your vision below! 👇 Like if you’re bullish on Tesla’s multi-product future | Repost to spread the hype | Follow for daily Tesla energy 🔥 #Tesla #Optimus #RobotRevolution #FSD #SustainableFuture #Cybertruck

Kendall

15,501 Aufrufe • vor 2 Monaten