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Reinforcement Learning on Hardware from Sim-to-Real (Rotary Inverted Pendulum)
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Kevin Wood | Robotics & AI
662 posts
Kevin Wood | Robotics & AI
@KWRoboticsAI
10+ years in robotics, AI/Ml, computer vision and controls. 64k+ subscribers on YouTube. Subscribe to learn more! Link down below 👇
Katılım Kasım 2020
920 Takip Edilen658 Takipçiler
Not surprised at all. Anything that requires installation at home means higher barrier to entry. Plus imagine having to customize for every different home…sounds expensive and low ROI
PicoCreator - AI builder @ 🇸🇬 🔜 🌉@picocreator
Surprised we have yet to see a robotics startup pitching a household rail system, which skips all the current hardware problems of batteries and legs This lets it focus on the MVP, doing chores (in house), and hands.
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Instead of relying on existing video generation model as the backbone, LingBot VA 2.0 pertains their model from scratch. This makes it more physically grounded and more suitable for robot control actions. Lots of design decisions were made to greatly improve inference time making the robots move faster and smoother. Full coverage on my YouTube channel! Link in bio
Yinghao Xu@YinghaoXu1
🚀 LingBot-VA 2.0 is here! After half a year of teamwork, we're thrilled to release LingBot-VA 2.0 — a native video-action foundation model for generalizable robot control. Unlike prior world-action models that retrofit generic video generators for robot control, LingBot-VA 2.0 is natively pretrained from scratch as a video-action foundation model. Three key insights: 🌍 Native Video-Action Pretraining for learning world knowledge that enables strong generalization. 🧩 Semantic Visual-Action Tokenizer for more accurate action prediction with robust prompt following ⚡ Foresight Reasoning enables the robot to think ahead while acting, delivering continuous, responsive control without interrupting execution. It runs in real time on consumer-grade GPUs, supports up to 150 Hz control, and generalizes to unseen tasks. 👇 Demos below
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@KWRoboticsAI Pretty much spot on. Hardwear is only an API to the physical world. Be fascinating to see how they manage the sim to real data drift. 👍
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Impressive design, but I think it’s overhyped. To me a good robot hand design will be one that can be paired the best with AI models. As we know the sim to real gap is a challenging one, hence the move to direct drive hands in the recent years. Until I see some autonomous demos with these hands, I’m not very convinced about their capabilities. Also, can you imagine the challenge mass producing these?
Bernt Bornich@BerntBornich
Introducing NEO’s 25 Degrees of Freedom, tendon-driven hands — nearing or surpassing human-level dexterity, strength, speed, and reliability. For seventy years, robotics worked around the hand problem. The humanoid bet is the reverse: it lives or dies at the fingertips.
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@sreak1089 You can tell by the complexity of the design
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@KWRoboticsAI What are the specific mass production challenges that you foresee @KWRoboticsAI ?
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New VLA just came out surpassing Pi 0.5 and GR00T N1.7! Works on 20 different types of robots. Full coverage on my YouTube channel! Link in bio. You can try out the LingBot-VLA 2.0 on their GitHub or hugging face
Robbyant@robbyant_brain
🤖 LingBot-VLA 2.0 is now open-source — our next-gen embodied foundation model. 🔷 60,000 hours of high-quality pretraining data — combining curated robotic demonstrations and egocentric human operation videos 🔷 20 robot configurations across 17 brands — Astribot, Leju, Unitree, Franka, Fourier, Realman, and more 🔷 Whole-body DoF: heads, waists, dexterous hands, and mobile bases — enabling far more complex task scenarios 🔷 Inference under 130ms on RTX 4090 — developer events launching soon #EmbodiedAI #Robotics #OpenSource #VLA
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Trying to break the robot hand from Rysen Robotics at ICRA 2026
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Using a robot hand to do some hammering! From Rysen Robotics at ICRA 2026
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TARS sensehub ecosystem for robot data collection used to train robot policies. Lot of options to either have a POV from the head or hands
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@iuliaferoli Awesome! Btw saw you went to Vienna, missed your YouTube comment. Hope to see you at a future conference
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The duck is ALIVE 🦆
4 days of soldering, one heatwave, and several electrical exorcisms later, my Open Duck Mini droid is fully assembled. IMU calibrated, all 14 servos responding, speaker working.
First time soldering a Raspberry Pi too.
We learned a lot.
Full build video is live 👇
youtu.be/tt-g_fi-eGU
Next up: teaching her to walk (and eventually, walk on sand dunes 🎬)
#openduck created by @antoinepirrone
kit by @tnkrdotai
coded in @pycharm
#robotics #physicalAI
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Just wrapped up an unforgettable two-week robotics marathon across China, and I still can’t quite believe it’s over.
Every single day of the trip and even now looking back, I was overwhelmed with gratitude for this incredible privilege. I love this robotics/AI world, and this journey reminded me exactly why.
Gaining access to 18 of China’s top humanoid robotics companies in such a short time felt almost surreal. It was a rare and special experience.
None of it would have happened without the amazing people who made it possible. Huge thanks to @TheHumanoidHub, @XRoboHub, our interpreter Winnie and everyone else who opened doors, shared insights, and supported us along the way. You made this trip truly beautiful.
I’ve long been fascinated by China’s robotics ecosystem and always wanted to visit someday. Everyone I spoke with in the US and elsewhere kept saying “If you’re serious about this field you have to see China.”
Spending two intense weeks on the ground, visiting different cities and meeting brilliant founders and engineers was deeply inspiring. I loved every moment of it.
It all started earlier this year in SF. @TheHumanoidHub and I kept crossing paths, diving deep into robotics conversations and I knew he was planning this journey. When he turned to me and asked “Want to come?” I said yes immediately and booked my ticket the same day. Future me is still thanking past me for that decision.
The trip was wonderfully intense and ridiculously fun. I’ll never forget sharing takeout pizza in taxis while racing to the next company, passionately talking about the future of robotics the whole way. Those moments were pure joy.
There’s so much I saw, felt, and learned. Before the memories fade I’ll be sharing more of it here. And yes — we have more content coming so stay tuned😎🤖
The Humanoid Hub@TheHumanoidHub
That's a wrap on an amazing 17-day journey across China. I didn't do this alone. My friend @dolylupec came along for the whole ride. She's got a background in AI and robotics investing, and I'm grateful she chronicled the tours on her account and pitched in on my channel. @XRoboHub is one of the most resourceful people I know. He understands the humanoid space well and helped us schedule most of the important visits, then came along too. And a shout-out to @Robo_Tuo, who encouraged me to do this trip and supported us all along. It was my first time in China, and the trip was packed so tight with tours, we didn’t get to do much touristy stuff. Can't complain. It was a hell of a ride. 4 cities on the mainland. 18 humanoid makers. Warm, welcoming people everywhere. We had the privilege of chatting with founders, senior engineers, researchers, and marketing and strategy leads, a lot of whom said yes on very short notice. Grateful for that. The energy is extraordinary. It's the trifecta: engineering talent, a deep supply chain, and breakneck adoption of new tech. There's intense competition, but underneath it, a real spirit of sharing and collaboration. I've got a series of tour videos lined up, dropping over the coming days. And something tells me this won't be my last trip. Robotics and embodied AI are just getting started. The next ten years are going to reshape how we think about physical work. We ended on the Great Wall, a 2,000 year old marvel of engineering, after two weeks staring at the next one. The robots are coming. I just went to meet them first.
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That's a wrap on an amazing 17-day journey across China.
I didn't do this alone. My friend @dolylupec came along for the whole ride. She's got a background in AI and robotics investing, and I'm grateful she chronicled the tours on her account and pitched in on my channel.
@XRoboHub is one of the most resourceful people I know. He understands the humanoid space well and helped us schedule most of the important visits, then came along too.
And a shout-out to @Robo_Tuo, who encouraged me to do this trip and supported us all along.
It was my first time in China, and the trip was packed so tight with tours, we didn’t get to do much touristy stuff. Can't complain. It was a hell of a ride.
4 cities on the mainland. 18 humanoid makers. Warm, welcoming people everywhere. We had the privilege of chatting with founders, senior engineers, researchers, and marketing and strategy leads, a lot of whom said yes on very short notice. Grateful for that.
The energy is extraordinary. It's the trifecta: engineering talent, a deep supply chain, and breakneck adoption of new tech. There's intense competition, but underneath it, a real spirit of sharing and collaboration.
I've got a series of tour videos lined up, dropping over the coming days.
And something tells me this won't be my last trip. Robotics and embodied AI are just getting started. The next ten years are going to reshape how we think about physical work.
We ended on the Great Wall, a 2,000 year old marvel of engineering, after two weeks staring at the next one.
The robots are coming. I just went to meet them first.
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Does palntir still have an edge in AI with LLMs and agents getting better by the day?
CapexAndChill@CapexAndChill
So the market fears that AI labs like OpenAI and Anthropic will render $PLTR's enterprise software obsolete. When asked if Anthropic is a competitor, Karp said that most of the capabilities frontier models discuss in public are currently running on Palantir. At its current scale, Palantir acts as the underlying infra for these AI labs, functioning as a 'nation state' in the ecosystem. Unlike LLMs, Karp says high-end enterprises such as defense, aerospace, manufacturing require deterministic execution where AI errors are catastrophic. Commercial enterprises are becoming disillusioned with frontier AI labs because raw models lack the capacity to handle strict security, data integration, and ontology, leaving them almost useless for complex corporate infra.
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Looks like we don’t need to wait for Mythos/Fable
OpenAI@OpenAI
GPT‑5.6 Sol sets a new state of the art on Terminal‑Bench 2.1, which tests complex command-line workflows requiring planning, iteration, and tool coordination.
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