Yobow wang @Slientea98
Joined November 2021-
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Another small humanoid robot. PHYBOT, a Beijing-based company, has launched the PHYBOT C1, a cute humanoid robot designed for elderly care, companionship, and explanations. Standing 1.28 meters tall and weighing 28 kg, it features 25 degrees of freedom, a facial light, and can…
While most people believe that harmonic and planetary are the ultimate hardware solution for humanoid robots, cycloidal actuators, especially QDD cycloidal is becoming a trend.It combines the advantages of harmonics and planetary at an affordable price.
🧠With the shift in humanoid control from pure RL to learning from demonstrations, we take a step back to unpack the landscape. 🔗breadli428.github.io/post/lfd/ 🚀Excited to share our blog post on Feature-based vs. GAN-based Learning from Demonstrations—when to use which, and why it…
At this point in time -- this is a completely stupid, pointless, and counterproductive demo. Not a good look. Yes, it is now well established that we know how to train robots to walk and stand under a variety of disturbances.
At this point in time -- this is a completely stupid, pointless, and counterproductive demo. Not a good look. Yes, it is now well established that we know how to train robots to walk and stand under a variety of disturbances.
Now in Nature! 🚀 Our method learns a controllable 3D model of any robot from vision, enabling single-camera closed-loop control at test time! This includes robots previously uncontrollable, soft, and bio-inspired, potentially lowering the barrier of entry to automation! Paper:…
All the more so.
Here’s our latest RL update: Natural Mogging (thread below!)
Here’s our latest RL update: Natural Mogging (thread below!)
Interested in simulated characters traversing complex terrains? PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers Project page: michaelx.io/parc/index.html with: @YiShi_333, @KangkangYin, and @xbpeng4 @siggraph 2025 Conference Paper 1/
I missed this, Sangbae Kim just joined Meta doing robotics. Sangbae advised Ben Katz, they did the MIT Cheetah together, and he’s been doing that biomemetic quasidirect drive motor guff for over a decade. Meta is gonna do some shit.
Why do birds bob their heads? Why is their running style different to ours? Just want to see untrained chickens on a treadmill? Watch this new video! It's in Dutch, with English subtitles. Watch till the end to see me get interrupted by loud chickens youtu.be/cxewQ1GYcA4?si…
We (@Amidos2006, @Matt_Barthet, @gallorob2, @SentientDesigns, @togelius, @yannakakis) would like to announce the Procedural Content Generation Benchmark (PCG Benchmark). The PCG Benchmark allows users working in PCG research to test their algorithms against a variety of games.
mink 0.0.6 is out! We've added support for equality constraints which is particularly useful for closed-chain mechanisms like 4-bar linkages. Plus a bunch more humanoid focused examples.
Something new tomorrow...
🚀 Can we make a humanoid move like Cristiano Ronaldo, LeBron James and Kobe Byrant? YES! 🤖 Introducing ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills Website: agile.human2humanoid.com Code: github.com/LeCAR-Lab/ASAP
Can we build a generalist robotic policy that doesn’t just memorize training data and regurgitate it during test time, but instead remembers past actions as memory and conditions its decisions on them?🤖💡 Introducing SAM2Act—a multi-view robotic transformer-based policy that…
🚨 BIG MILESTONE! 🚨 @deepseek_ai R1 brings us closer to robots with reasoning abilities 🤖✨ Inspired by RL at scale, we hit highest rewards in just 5 mins of async + distributed RL training on robotics task! 🏆⚡ It used to take hours on H100 before. Game changer. 🚀
Excited to share our recent work: Dynamic Bipedal Loco-manipulation using Oracle Guided Multi-mode Policies with Mode-transition Preference Website: indweller.github.io/ogmplm/ Preprint: arxiv.org/abs/2410.01030 Video: youtu.be/gfDaRqobheg?fe… 🧵(1/n)
"Move 37" is the word-of-day - it's when an AI, trained via the trial-and-error process of reinforcement learning, discovers actions that are new, surprising, and secretly brilliant even to expert humans. It is a magical, just slightly unnerving, emergent phenomenon only…
Human videos contain rich data on how to complete everyday tasks. But how can robots directly learn from human videos alone without robot data? We present MT-π, an IL framework that takes in human video and predicts actions as 2D motion tracks. portal.cs.cornell.edu/motion_track_p… 🧵1/6
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inControl podcast @inControlpdcst
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