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whoa unitree humanoid robot becomes extremely good at everything he does. Full fire from start to finish! Top-tier motion control The OmniXtreme framework keeps pushing the Unitree G1 body past its physical limits. From : @ 铁卫士

12,827 次观看 • 2 个月前 •via X (Twitter)

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Unitree Robotics just filed to go public on March 20th targeting a $7 billion valuation. Most people have no idea what this company actually is. Here is why this might be the most important robotics IPO of the decade. Unitree shipped 5,500 humanoid robots in 2025. Figure AI shipped roughly 150. Agility Robotics shipped roughly 150. Unitree did $246 million in revenue last year, up 335% year over year, and they are actually profitable. Figure AI is valued at $39 billion with near zero revenue and is still private. Unitree wants $7 billion with real numbers. The price point is what separates them from everyone else. Their G1 humanoid sells for $13,500. Competitors charge $50,000 to $130,000. Their newest R1 humanoid launching in April starts at $4,900. Nothing comparable exists at that price anywhere in the world. They hold roughly 32% of the global humanoid market and 70% of the quadruped market. The moat is vertical integration. They self develop over 90% of core components including motors, reducers, controllers, sensors, and all software. Real clients include PetroChina, Sinopec, State Grid, and China Mobile. This is not a research project. Product is shipping at scale. This is listing on China's STAR Market, not Hong Kong, not the US, which makes access extremely difficult for international investors. The risks are real. The US House Select Committee on the CCP has formally requested Unitree be blacklisted. Their robots appeared in PLA military exercises in 2024. Tariffs have already nearly tripled the US price of the G1. $TSLA Optimus is targeting sub $20,000 pricing with automotive scale manufacturing backed by $NVDA compute. If they execute, the price advantage shrinks fast. But this is still the only profitable pure play humanoid robotics company in the world growing at 335% a year, valued at a fraction of its loss making peers. Goldman projects the humanoid market at $38 billion by 2035. Morgan Stanley goes to $5 trillion by 2050. Unitree currently holds the largest market share of any humanoid manufacturer on the planet. Full breakdown coming soon. $TSLA $NVDA

KawzInvests

74,078 次观看 • 4 个月前

Not a preplanned motion sequence. A robot deciding mid-jump what to do next. [📍 paper + demo] Researchers just showed a humanoid doing real parkour using only onboard perception. No motion script, no fixed obstacle layout. The system is called Perceptive Humanoid Parkour (PHP). Instead of memorizing a path, the robot reads depth from its cameras and continuously chooses actions. Step, vault, climb, or roll depending on what geometry appears in front of it. To make that possible, they combine three ideas: First, they stitch together human motion clips into long movement references so the robot learns fluid transitions instead of isolated tricks. Second, they train tracking policies with reinforcement learning so contacts land at the right time and the robot keeps balance during dynamic moves. Finally, everything is distilled into one perception policy that runs directly from depth input to action selection. The result on a Unitree G1: about 3 m/s vaults wall climbs up to 1.25 m nearly one minute continuous obstacle traversal adapting when obstacles move What matters is not the tricks. It is the shift in capability. Earlier humanoids executed motions. This one navigates situations. Once robots react to geometry instead of replaying trajectories, environments stop needing to be predictable. Warehouses, homes, and outdoors suddenly become the same problem. Thanks for sharing, Zhen Wu! Paper + demo: ——— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

22,080 次观看 • 5 个月前

New framework: Kick down your robot, it will get back up every time 🥋 Chinese startup RoboParty is a Beijing startup founded April 2025 by Huang Yi, originally shipping ROBOTO ORIGIN, the world's first full-stack open-source bipedal humanoid. They released UFO: Unsupervised Reinforcement Learning Framework for Humanoid Control. DEFINITIONS -> what differs is where the learning signal comes from: - SUPERVISED: humans supply the right answers (labels), the model imitates them. - UNSUPERVISED: no answer key, the model finds structure in raw data on its own. - REINFORCEMENT LEARNING: no answer key either, the model tries things and a reward scores each attempt. → UNSUPERVISED RL: trial and error where the agent invents its own rewards, instead of engineers hand-writing one per task. REPRESENTATION LEARNING: compress raw states into a useful internal map. TEMPORAL DISTANCE: distance on that map is "how many steps from A to B." CONTRASTIVE: trained by pulling together what's close in time, pushing apart what isn't. -> CONTRASTIVE TEMPORAL-DISTANCE REPRESENTATION LEARNING: the model builds an internal map of body states where distance means how many steps it takes to get from one to another. It is trained by contrast: states that occur close together in a movement get pulled together in the map, randomly paired states get pushed apart. UFO is an open-source training framework that teaches humanoid robots skills, like getting up, walking, goal-reaching, teleoperation, without reference motions -> no motion-capture or human-video demonstrations to imitate. Its core is TeCH, a contrastive temporal-distance representation-learning algorithm: the robot explores, builds pseudo-goals by temporal rolling, and learns goal-conditioned policies from a single unified progress reward. One framework trains five different robots (Unitree G1/H1, RoboParty RP0/RP1, AgiBot X2) with automatic config conversion in ~2–3 hours per robot! The real novelty here "no demonstrations at all". No data-collection arms race,the dominant humanoid-locomotion recipe is tracking: imitate mocap/retargeted-human reference trajectories. The robot self-generates goals from its own exploration and learns from a progress reward, needing zero reference motion data. Everybody else is fighting over data acquisition, while this team just teleports out of the race entirely (inb4 "competition is for losers 💀 ). This strategy reminds me of the DeepSeek playbook applied to robots: open-source the whole stack to become the global default and commoditize everyone else. RoboParty is giving away hardware and now control software (UFO) to be the Android of humanoids. Yet another reason for the US to ban Chinese open models perhaps 🥶 ? What I also really like about this approach is the cross-embodiment infrastructure, one framework trains Unitree G1/H1, RoboParty RP0/RP1, and AgiBot X2 with automatic configuration conversion. Just like Physical Intelligence, RoboParty seems to place itself as a neutral hardware agnostic middle man. Also woth mentioning: their ability ot perform stable skill injection, e.g. adding a cartwheel without forgetting how to walk. A common failure of RL humanoid policies is that teaching a new agile skill destabilizes the existing ones (catastrophic forgetting). UFO claims you can inject rare motions (cartwheel) without collapsing learned behavior. If it holds, that's a significant incremental/continual skill-learning! But again, I have to underline it: no arXiv, no external validation, no success-rate numbers. -> robotics badely needs an independent unbiased evaluator imho. Still, look at that cool demo: robot is getting kicked and pushed around (serious disturbance) during teleoperation (controlled the person at the back wearing the VR headset), and still managed to always get back up. This is some serious demonstration of stability and robustness!

Léo

35,705 次观看 • 12 天前