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In the last month, we’ve been building an open-source framework for robot learning and sim-to-real transfer, made for RL whole-body control from simple walking to complex human imitation Check out the details on HN: Get started in 5 minutes ⬇️

75,983 görüntüleme • 1 yıl önce •via X (Twitter)

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K-Scale Labs profil fotoğrafı
K-Scale Labs1 yıl önce

Try it now:

K-Scale Labs profil fotoğrafı
K-Scale Labs1 yıl önce

Want to help solve some of the biggest problems in robotics? Join the leaderboard. Train an RL policy tonight; watch it run on a real humanoid tomorrow:

K-Scale Labs profil fotoğrafı
K-Scale Labs1 yıl önce

We would love to hear your feedback :) What would you like your humanoid robot to do?

The Rundown AI profil fotoğrafı
The Rundown AI1 yıl önce

If you're not learning AI in 2025, you're falling behind. Join 1,000,000+ early adopters reading and learn AI in just 5 minutes a day (for free).

Kevin Zakka profil fotoğrafı
Kevin Zakka1 yıl önce

No brax and mujoco playground acknowledgment is crazy

btaba profil fotoğrafı
btaba1 yıl önce

Would y'all be down to donate a few of your robots to our lab? Would love to try 'em out on the latest and greatest MuJoCo/MJX releases

Pierre-Louis Biojout (PLB) profil fotoğrafı
Pierre-Louis Biojout (PLB)1 yıl önce

That's very cool but how can we get a bot to test? Looks like it's preorders only...

David Hansen 🇺🇸🇳🇿 profil fotoğrafı
David Hansen 🇺🇸🇳🇿1 yıl önce

Ordered

Benzer Videolar

Today, we're joined by Nikita Rudin, co-founder and CEO of Flexion to discuss the gap between current robotic capabilities and what’s required to deploy fully autonomous robots in the real world. Nikita explains how reinforcement learning and simulation have driven rapid progress in robot locomotion—and why locomotion is still far from “solved.” We dig into the sim2real gap, and how adding visual inputs introduces noise and significantly complicates sim-to-real transfer. We also explore the debate between end-to-end models and modular approaches, and why separating locomotion, planning, and semantics remains a pragmatic approach today. Nikita also introduces the concept of "real-to-sim", which uses real-world data to refine simulation parameters for higher fidelity training, discusses how reinforcement learning, imitation learning, and teleoperation data are combined to train robust policies for both quadruped and humanoid robots, and introduces Flexion's hierarchical approach that utilizes pre-trained Vision-Language Models (VLMs) for high-level task orchestration with Vision-Language-Action (VLA) models and low-level whole-body trackers. Finally, Nikita shares the behind-the-scenes in humanoid robot demos, his take on reinforcement learning in simulation versus the real world, the nuances of reward tuning, and offers practical advice for researchers and practitioners looking to get started in robotics today. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 04:07 - Is robot locomotion solved? 06:04 - Sim-to-real gap 08:58 - Adding semantics to policies 09:42 - Modular vs end-to-end architectures 10:29 - Planner model 12:21 - Adapting RL techniques from quadrupeds to humanoids 15:39 - Behind robot demos 18:09 - Humanoid robots in home environments 22:03 - Training approach 23:56 - VLA models 27:59 - Closing the sim-to-real gap 32:55 - Task orchestration using VLMs 36:38 - Tool use 38:10 - Model hierarchy 43:37 - Simulator versus simulation environment 44:57 - Combining imitation learning and reinforcement learning 46:42 - RL in real world versus RL in simulation 52:58 - Reward tuning and value functions in robotics 56:38 - Predictions 1:00:10 - Humanoids, quadropeds, and wheeled platforms 1:02:45 - Advice, recommended robot kits, and community pla

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