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A sub-$500 bionic hand is finally hitting the market. 🖐️🤖 SoftSync just dropped the FlexHand V1, a 6-DoF tendon-driven gripper that weighs just 314g but can snatch up a 2500g (5.5 lbs) load. This 1:1 human-scale hand is built with a proprietary soft-lattice structure, making it waterproof and rugged...

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RoboHub🤖

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14,654 次观看 • 5 个月前 •via X (Twitter)

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Open-Source Robotic Hand That Costs Just $300. California-based company TetherIA.ai has officially launched its open-source robotic hand, the Aero Hand Open. Designed to be the most capable at the lowest cost, this hand is a new tool for embodied AI research. In the clip, it demonstrates impressive dexterity by grasping an M5 screw, picking up an iPhone, and opening a soda can. Key Features: ► Price: $314.00 ► Open Source: CAD, electronics, and firmware are all open source. ► Actuation: 7 Active Degrees of Freedom (16 joints) with a tendon-driven design. ► Weight: Under 400 grams. ► Purpose: Built for embodied AI research to make dexterous manipulation accessible to all. For more details and to purchase:
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Open-Source Robotic Hand That Costs Just $300. California-based company TetherIA.ai has officially launched its open-source robotic hand, the Aero Hand Open. Designed to be the most capable at the lowest cost, this hand is a new tool for embodied AI research. In the clip, it demonstrates impressive dexterity by grasping an M5 screw, picking up an iPhone, and opening a soda can. Key Features: ► Price: $314.00 ► Open Source: CAD, electronics, and firmware are all open source. ► Actuation: 7 Active Degrees of Freedom (16 joints) with a tendon-driven design. ► Weight: Under 400 grams. ► Purpose: Built for embodied AI research to make dexterous manipulation accessible to all. For more details and to purchase:

RoboHub🤖

45,830 次观看 • 11 个月前

Shanghai MATRIX ROBOTICS has released a video of its new generation general-purpose humanoid robot: MATRIX-3. Much more compact than the first generation, without a protruding "rear end," it features an impressive futuristic design, 27-DOF dexterous hand with tendon-driven joints, 3D soft fabric bionic skin (claimed to have tactile sensing), and incorporates a new neural network that allows for zero-shot learning, enabling it to perform tasks in complex environments. It is planned for use in general scenarios such as manufacturing plants, logistics warehouses, and retail supermarkets. However, this is currently just a video, and its actual performance remains to be seen, especially the rapid, sensitive, and free movements of the hands as shown in the video: this would truly be a huge breakthrough.
0:56

Shanghai MATRIX ROBOTICS has released a video of its new generation general-purpose humanoid robot: MATRIX-3. Much more compact than the first generation, without a protruding "rear end," it features an impressive futuristic design, 27-DOF dexterous hand with tendon-driven joints, 3D soft fabric bionic skin (claimed to have tactile sensing), and incorporates a new neural network that allows for zero-shot learning, enabling it to perform tasks in complex environments. It is planned for use in general scenarios such as manufacturing plants, logistics warehouses, and retail supermarkets. However, this is currently just a video, and its actual performance remains to be seen, especially the rapid, sensitive, and free movements of the hands as shown in the video: this would truly be a huge breakthrough.

CyberRobo

192,751 次观看 • 7 个月前

Xynova just launched Flex 2, its second-gen hybrid dexterous hand — and Xiaomi is already on the cap table. 🤖✋ The spec sheet is sharp: 23 DOF, 400g palm, ±0.1mm repeatability, 0.05N force control, 12kg single-hand grasp load, and multimodal sensing for adaptive grasping and slip detection. Xynova is not only building a robotic hand. It is building the manipulation stack around it: micro linear actuators, joint modules, sensing, control, and developer-facing integration. The Xiaomi link is confirmed. Xiaomi Strategic Investment joined Xynova’s angel round and continued adding in the Pre-A round. What is not confirmed: whether Xiaomi’s own robot hand uses Xynova’s technology. That detail still has no public proof. But the direction is clear: Xiaomi is putting money into the component layer that decides whether humanoids can actually handle objects, tools, and daily tasks. This is the part of robotics that rarely looks flashy on stage. But it decides what a robot can really do with its hands.
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Xynova just launched Flex 2, its second-gen hybrid dexterous hand — and Xiaomi is already on the cap table. 🤖✋ The spec sheet is sharp: 23 DOF, 400g palm, ±0.1mm repeatability, 0.05N force control, 12kg single-hand grasp load, and multimodal sensing for adaptive grasping and slip detection. Xynova is not only building a robotic hand. It is building the manipulation stack around it: micro linear actuators, joint modules, sensing, control, and developer-facing integration. The Xiaomi link is confirmed. Xiaomi Strategic Investment joined Xynova’s angel round and continued adding in the Pre-A round. What is not confirmed: whether Xiaomi’s own robot hand uses Xynova’s technology. That detail still has no public proof. But the direction is clear: Xiaomi is putting money into the component layer that decides whether humanoids can actually handle objects, tools, and daily tasks. This is the part of robotics that rarely looks flashy on stage. But it decides what a robot can really do with its hands.

RoboHub🤖

71,176 次观看 • 3 个月前

Westworld-like hand Suzhou-bassed Rochu Robotics just tested a biomimetic hand that is completely different from the rigid motor-driven dexterous hands on the market: Pneumatic/Fluid-driven artificial muscles + 1:1 human skeletal structure. These artificial muscles contract when pressurized,just like biological ones --pulling on biomimetic tendons to move the joints with smooth, natural motion. Just 4 biomimetic muscles can easily lift a 3kg kettlebell, and demonstrate the versatility of flexible grasping…while also acing fast finger challenges (peace sign, rock on, counting, etc.) almost matching a real human hand. True compliance and safety in human-robot interaction,no more cold, stiff mechanics. This one is developing real life-like movement logic.
1:10

Westworld-like hand Suzhou-bassed Rochu Robotics just tested a biomimetic hand that is completely different from the rigid motor-driven dexterous hands on the market: Pneumatic/Fluid-driven artificial muscles + 1:1 human skeletal structure. These artificial muscles contract when pressurized,just like biological ones --pulling on biomimetic tendons to move the joints with smooth, natural motion. Just 4 biomimetic muscles can easily lift a 3kg kettlebell, and demonstrate the versatility of flexible grasping…while also acing fast finger challenges (peace sign, rock on, counting, etc.) almost matching a real human hand. True compliance and safety in human-robot interaction,no more cold, stiff mechanics. This one is developing real life-like movement logic.

CyberRobo

28,240 次观看 • 2 个月前

Forget simple AGV transport—the game is now robots that can both "move" and "operate" heavy loads. 🤖💪 Galbot just dropped the S1, its first dual-arm robot with a 50kg (approx. 110 lbs) max payload for fully autonomous work. Close behind, Lumos and Damon Technology launched their own logistics solutions, also hitting that 50kg (approx. 110 lbs) dual-arm capacity to meet heavy-duty demands. Tackling a 50kg load isn't just about strength; mechanical complexity grows exponentially with every degree of freedom. This marks the evolution from basic "leg" movement to full "hand-eye-foot coordination" for heavy industrial tasks. #Robotics #Galbot #Lumos #Humanoid #IndustrialAutomation #Logistics #Tech
0:46

Forget simple AGV transport—the game is now robots that can both "move" and "operate" heavy loads. 🤖💪 Galbot just dropped the S1, its first dual-arm robot with a 50kg (approx. 110 lbs) max payload for fully autonomous work. Close behind, Lumos and Damon Technology launched their own logistics solutions, also hitting that 50kg (approx. 110 lbs) dual-arm capacity to meet heavy-duty demands. Tackling a 50kg load isn't just about strength; mechanical complexity grows exponentially with every degree of freedom. This marks the evolution from basic "leg" movement to full "hand-eye-foot coordination" for heavy industrial tasks. #Robotics #Galbot #Lumos #Humanoid #IndustrialAutomation #Logistics #Tech

RoboHub🤖

27,177 次观看 • 7 个月前

Awesome! This is literally a superhuman hand. 🤖✋ ChangingTek Robotics just dropped a dexterous hand that actually morphs between left and right hand configurations on the fly. It uses cable-driven tech to pull off shapes and reverse bends that human hands can't even dream of doing. The power is real too, with a 50N grip force and 10N at the fingertips while staying precise within 0.1N. It is kind of like a rigid octopus, meaning it is tough enough to crank a wrench or screwdriver but smooth enough to grab a drink. Having this much range of motion in a single hand basically doubles the efficiency of any robotic arm in tight spots. Source: ChangingTek Robotics #Robot #Humanoid #Robotics #AI #EmbodiedAI #PhysicalAI #ChangingTek #DexterousHand #Bionics
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Awesome! This is literally a superhuman hand. 🤖✋ ChangingTek Robotics just dropped a dexterous hand that actually morphs between left and right hand configurations on the fly. It uses cable-driven tech to pull off shapes and reverse bends that human hands can't even dream of doing. The power is real too, with a 50N grip force and 10N at the fingertips while staying precise within 0.1N. It is kind of like a rigid octopus, meaning it is tough enough to crank a wrench or screwdriver but smooth enough to grab a drink. Having this much range of motion in a single hand basically doubles the efficiency of any robotic arm in tight spots. Source: ChangingTek Robotics #Robot #Humanoid #Robotics #AI #EmbodiedAI #PhysicalAI #ChangingTek #DexterousHand #Bionics

RoboHub🤖

13,855 次观看 • 5 个月前

A robot hand grasp over 500 totally new objects without fail? Zero-shot, single-view & super reliable ⬇️ + Paper Grasping random objects is hard for robots, especially when shapes, weights, and materials vary. RobustDexGrasp solves this with a smart new way of seeing and controlling the hand, leading to near-perfect grasping, even in noisy or cluttered scenes. Thank you for sharing, Hui Zhang 🙏 Follow him!! What makes it special ✅ Grabs 500+ unseen objects with 94.6% success using only single-view input ✅ Learns local shapes, not full geometry, for better generalization ✅ Trained with just 35 objects in sim but works in the real world with hundreds more ✅ Adapts to noise, unexpected forces, and even plays chess with VLM planning It shows that smart sensing and adaptive control can take dexterous grasping to the next level. Project: Paper:
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A robot hand grasp over 500 totally new objects without fail? Zero-shot, single-view & super reliable ⬇️ + Paper Grasping random objects is hard for robots, especially when shapes, weights, and materials vary. RobustDexGrasp solves this with a smart new way of seeing and controlling the hand, leading to near-perfect grasping, even in noisy or cluttered scenes. Thank you for sharing, Hui Zhang 🙏 Follow him!! What makes it special ✅ Grabs 500+ unseen objects with 94.6% success using only single-view input ✅ Learns local shapes, not full geometry, for better generalization ✅ Trained with just 35 objects in sim but works in the real world with hundreds more ✅ Adapts to noise, unexpected forces, and even plays chess with VLM planning It shows that smart sensing and adaptive control can take dexterous grasping to the next level. Project: Paper:

Ilir Aliu

37,980 次观看 • 1 年前

Hands full of dexterity! 🪬 It is well known that agile, versatile hands for robots are one of the Holy Grails in robotics today. Grippers and robot hands are often underestimated, and they are the actuators behind correct and accurate picking, or placing. This robotic hand was created at the Korea University of Technology and Education. 🇰🇷 The 5-finger, 12-DoF (degrees of freedom) robotic hand features 3-DoF each for the thumb, index, and middle fingers, while the ring and little fingers share a combined 3 DoF. It uses BLDC motors with ball-screw actuators and includes a clutch mechanism to absorb impacts and prevent overload. Now, a challenge for you - guess the year! Kudos to all gripper/hand-developing companies! WE CAN'T PICK WITHOUT YOU! 🏋🏼 ~~~ ♻️ RT to help 1 robot find a new workplace.
0:54

Hands full of dexterity! 🪬 It is well known that agile, versatile hands for robots are one of the Holy Grails in robotics today. Grippers and robot hands are often underestimated, and they are the actuators behind correct and accurate picking, or placing. This robotic hand was created at the Korea University of Technology and Education. 🇰🇷 The 5-finger, 12-DoF (degrees of freedom) robotic hand features 3-DoF each for the thumb, index, and middle fingers, while the ring and little fingers share a combined 3 DoF. It uses BLDC motors with ball-screw actuators and includes a clutch mechanism to absorb impacts and prevent overload. Now, a challenge for you - guess the year! Kudos to all gripper/hand-developing companies! WE CAN'T PICK WITHOUT YOU! 🏋🏼 ~~~ ♻️ RT to help 1 robot find a new workplace.

Lukas Ziegler

33,535 次观看 • 1 年前

NVIDIA just announced EgoScale 🤖🧠 NVIDIA Research has uncovered a log-linear scaling law for robot dexterity by pretraining VLA models on over 20,000 hours of egocentric human video This massive dataset is 20 times larger than previous efforts and proves that robot intelligence follows a predictable path: the more human data, the lower the loss The secret is a simple recipe combining large-scale human pretraining with a small amount of aligned human-robot mid-training to bridge the gap In testing, this method boosted the average success rate by 54% on a 22-DoF robotic hand compared to policies built without pretraining EgoScale also enables one-shot task adaptation and works across different hardware, suggesting that human motion is a universal motor prior for robots Website: Paper: Source: NVIDIA Research #Robot #Humanoid #Robotics #AI #EmbodiedAI #PhysicalAI #NVIDIA #EgoScale #GR00T
1:25

NVIDIA just announced EgoScale 🤖🧠 NVIDIA Research has uncovered a log-linear scaling law for robot dexterity by pretraining VLA models on over 20,000 hours of egocentric human video This massive dataset is 20 times larger than previous efforts and proves that robot intelligence follows a predictable path: the more human data, the lower the loss The secret is a simple recipe combining large-scale human pretraining with a small amount of aligned human-robot mid-training to bridge the gap In testing, this method boosted the average success rate by 54% on a 22-DoF robotic hand compared to policies built without pretraining EgoScale also enables one-shot task adaptation and works across different hardware, suggesting that human motion is a universal motor prior for robots Website: Paper: Source: NVIDIA Research #Robot #Humanoid #Robotics #AI #EmbodiedAI #PhysicalAI #NVIDIA #EgoScale #GR00T

RoboHub🤖

43,752 次观看 • 6 个月前

VLAI Robotics Launches High-Dexterity Dual-Arm Robot Starting at ~$5,500 USD VLAI Robotics, utilizing the open-source design of Japan’s OpenArm team, has introduced a high-dexterity, low-cost bimanual robotic system. The product is priced from 39,900 RMB (approximately 5,500 USD), making high-level research tools accessible to institutions and developers. The robot features a human-scale, arm-hand integrated design with up to 16 DOF (8 DOF per arm, including the gripper). It can handle a dual-arm peak payload of 12 kg while maintaining high precision. A key breakthrough is its ability to replicate human upper limb movement trajectories, which enhances data quality for imitation learning and remote operation. VLAI Robotics handled the domestic engineering, manufacturing, and VLA (Vision-Language-Action) algorithm integration, with quality control organized according to the strict standards of the OpenArm R&D framework. The robot is highly adaptable for research, education, and Physical AI training, supporting easy expansion for features like VR teleoperation.
1:43

VLAI Robotics Launches High-Dexterity Dual-Arm Robot Starting at ~$5,500 USD VLAI Robotics, utilizing the open-source design of Japan’s OpenArm team, has introduced a high-dexterity, low-cost bimanual robotic system. The product is priced from 39,900 RMB (approximately 5,500 USD), making high-level research tools accessible to institutions and developers. The robot features a human-scale, arm-hand integrated design with up to 16 DOF (8 DOF per arm, including the gripper). It can handle a dual-arm peak payload of 12 kg while maintaining high precision. A key breakthrough is its ability to replicate human upper limb movement trajectories, which enhances data quality for imitation learning and remote operation. VLAI Robotics handled the domestic engineering, manufacturing, and VLA (Vision-Language-Action) algorithm integration, with quality control organized according to the strict standards of the OpenArm R&D framework. The robot is highly adaptable for research, education, and Physical AI training, supporting easy expansion for features like VR teleoperation.

RoboHub🤖

12,961 次观看 • 10 个月前

Dexterous hands are one of the biggest challenges in robotics, with even Elon Musk noting their development accounts for half of a humanoid robot's total work. A new approach from the Y-Hand M1 bionic hand may be a significant step forward. Based on an international first-of-its-kind "bionic tension-compression theory," the M1 replaces traditional rigid hinges with a 6-DoF joint system and uses self-developed magnetic artificial muscles. This design allows it to perform complex, human-like tasks, including: ► Mastering all 33 Feix dexterous grasp categories. ► Precisely twirling playing cards and threading a needle with subtle human-like adjustments. ► Smoothly twisting bottle caps and turning book pages. The team behind the M1 is already working on the next-gen Y-Hand M3, aiming for around 70 degrees of freedom, believing that robotic hands may soon reach or even exceed human-level dexterity.
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Dexterous hands are one of the biggest challenges in robotics, with even Elon Musk noting their development accounts for half of a humanoid robot's total work. A new approach from the Y-Hand M1 bionic hand may be a significant step forward. Based on an international first-of-its-kind "bionic tension-compression theory," the M1 replaces traditional rigid hinges with a 6-DoF joint system and uses self-developed magnetic artificial muscles. This design allows it to perform complex, human-like tasks, including: ► Mastering all 33 Feix dexterous grasp categories. ► Precisely twirling playing cards and threading a needle with subtle human-like adjustments. ► Smoothly twisting bottle caps and turning book pages. The team behind the M1 is already working on the next-gen Y-Hand M3, aiming for around 70 degrees of freedom, believing that robotic hands may soon reach or even exceed human-level dexterity.

RoboHub🤖

64,560 次观看 • 11 个月前

Humanoids are finally getting a sense of touch that actually feels... human. 🤖 As third-gen bots like Optimus, XPENG, and Figure move from labs to the real world, the focus is shifting from just "walking" to "feeling." We are seeing a move toward tactile fabrics that can be literally tailored to a robot's frame like a custom suit. This isn't just about simple pressure sensors; it's about a full electronic skin with a folding radius under 0.2mm. That means a bot can detect a gram-level touch, sense textures, or even feel the slight slip of an object before it drops. The industrial impact is massive—the global market for these flexible sensors is projected to hit 27.4 billion RMB (approx. $3.98B) by 2030. Beyond just picking up boxes, this tech allows robots to sense the warmth and pressure of a hug or adjust balance through foot sensors. High-density arrays with dozens of sensors per square centimeter are becoming the new baseline for dexterity. By turning physical contact into high-fidelity data, these skins are the final key to making robots safe for our homes and hospitals. The future of robotics is looking a lot more flexible and a lot more sensitive.
0:25

Humanoids are finally getting a sense of touch that actually feels... human. 🤖 As third-gen bots like Optimus, XPENG, and Figure move from labs to the real world, the focus is shifting from just "walking" to "feeling." We are seeing a move toward tactile fabrics that can be literally tailored to a robot's frame like a custom suit. This isn't just about simple pressure sensors; it's about a full electronic skin with a folding radius under 0.2mm. That means a bot can detect a gram-level touch, sense textures, or even feel the slight slip of an object before it drops. The industrial impact is massive—the global market for these flexible sensors is projected to hit 27.4 billion RMB (approx. $3.98B) by 2030. Beyond just picking up boxes, this tech allows robots to sense the warmth and pressure of a hug or adjust balance through foot sensors. High-density arrays with dozens of sensors per square centimeter are becoming the new baseline for dexterity. By turning physical contact into high-fidelity data, these skins are the final key to making robots safe for our homes and hospitals. The future of robotics is looking a lot more flexible and a lot more sensitive.

RoboHub🤖

72,367 次观看 • 5 个月前

Boston Dynamics just dropped the production-ready Atlas at CES 2026, and the specs are next-level. This isn't just a prototype; it's a rugged, enterprise-grade worker built for the grind of real-world factories.🤖🦾 With 56 degrees of freedom and fully rotational joints, Atlas moves with "superhuman" flexibility. It features human-scale hands with tactile sensing in every finger and palm, allowing for high-precision manipulation. Plus, its 360-degree vision system ensures it always knows who's approaching, keeping human-robot collaboration safe. ➤ Rugged Performance: Lifts up to 110 lbs (approx. 50kg) and operates in extreme temps from -4°F to 104°F (-20°C to 40°C). It’s even water-resistant for industrial wash-downs. ➤ Smart Endurance: Runs for 4 hours on dual swappable batteries. When power gets low, it autonomously heads back to the station to swap its own batteries and get right back to work. ➤ Cloud Intelligence: New tasks can be trained in less than a day. Thanks to the Orbit platform, once one Atlas learns a skill, it’s instantly shared across the entire fleet. The scale is massive: the entire 2026 supply is already spoken for by Hyundai and a new AI partner. Hyundai is also building a specialized "Robotics Metal Plant" and a new factory capable of churning out 30,000 Atlas units annually. We’re officially watching the birth of a humanoid workforce at scale. Source: Boston Dynamics & Hyundai Group
3:47

Boston Dynamics just dropped the production-ready Atlas at CES 2026, and the specs are next-level. This isn't just a prototype; it's a rugged, enterprise-grade worker built for the grind of real-world factories.🤖🦾 With 56 degrees of freedom and fully rotational joints, Atlas moves with "superhuman" flexibility. It features human-scale hands with tactile sensing in every finger and palm, allowing for high-precision manipulation. Plus, its 360-degree vision system ensures it always knows who's approaching, keeping human-robot collaboration safe. ➤ Rugged Performance: Lifts up to 110 lbs (approx. 50kg) and operates in extreme temps from -4°F to 104°F (-20°C to 40°C). It’s even water-resistant for industrial wash-downs. ➤ Smart Endurance: Runs for 4 hours on dual swappable batteries. When power gets low, it autonomously heads back to the station to swap its own batteries and get right back to work. ➤ Cloud Intelligence: New tasks can be trained in less than a day. Thanks to the Orbit platform, once one Atlas learns a skill, it’s instantly shared across the entire fleet. The scale is massive: the entire 2026 supply is already spoken for by Hyundai and a new AI partner. Hyundai is also building a specialized "Robotics Metal Plant" and a new factory capable of churning out 30,000 Atlas units annually. We’re officially watching the birth of a humanoid workforce at scale. Source: Boston Dynamics & Hyundai Group

RoboHub🤖

28,554 次观看 • 7 个月前

MIT’s latest research just just gave Robots a great new skill to grip delicate yet heavy objects without breaking them. The big problem is robots often cannot pick up heavy but fragile things because gripping hard enough usually means crushing or slipping. A single gripper shape has to do 2 conflicting jobs, it must move freely to form a wrap, then resist big forces while staying gentle. Open-ended grippers can snake into place, but they usually need stiffness to hold, and stiffness puts load into a few high-pressure contact points. Very soft grippers spread pressure well, but they can buckle or slide when the object is heavy, so they fail at the holding stage. This new idea from MIT, called loop closure grasping, completely avoids that tradeoff. Loop closure grasping starts as an open loop, meaning the robot has a free tip that can snake around clutter and find a good wrap. Once the wrap is right, the tip locks back onto the base, turning the shape into a closed loop that surrounds the object. Now the load is carried mainly by tension, like a sling, so the loop can stay very floppy in bending and still hold strongly without pointy pressure. The prototype uses inflatable “vine” beams that grow from the tip, then a clamp and winch to fasten, tighten, and finally deflate for soft holding. That combo lets it do awkward grasps, like lifting a 6.8kg kettlebell from a cluttered bin or pulling an object from 3m away. --- Paper - science. org/doi/10.1126/sciadv.ady9581
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MIT’s latest research just just gave Robots a great new skill to grip delicate yet heavy objects without breaking them. The big problem is robots often cannot pick up heavy but fragile things because gripping hard enough usually means crushing or slipping. A single gripper shape has to do 2 conflicting jobs, it must move freely to form a wrap, then resist big forces while staying gentle. Open-ended grippers can snake into place, but they usually need stiffness to hold, and stiffness puts load into a few high-pressure contact points. Very soft grippers spread pressure well, but they can buckle or slide when the object is heavy, so they fail at the holding stage. This new idea from MIT, called loop closure grasping, completely avoids that tradeoff. Loop closure grasping starts as an open loop, meaning the robot has a free tip that can snake around clutter and find a good wrap. Once the wrap is right, the tip locks back onto the base, turning the shape into a closed loop that surrounds the object. Now the load is carried mainly by tension, like a sling, so the loop can stay very floppy in bending and still hold strongly without pointy pressure. The prototype uses inflatable “vine” beams that grow from the tip, then a clamp and winch to fasten, tighten, and finally deflate for soft holding. That combo lets it do awkward grasps, like lifting a 6.8kg kettlebell from a cluttered bin or pulling an object from 3m away. --- Paper - science. org/doi/10.1126/sciadv.ady9581

Rohan Paul

1,632,855 次观看 • 8 个月前

Milestone! We (robotic arms for gadgets assembly) finished the first commercial order, which brought the first revenue. Here are some learnings from this: The customer was a smart toy manufacturer. The task was to add a heatsink to Raspberry Pi. We received parts from them and returned the assembled modules back. Currently, it's done by teleoperation. Later it will be done by a remote employee via the Internet. Then it will be automated action by action, reducing the operator's time on this and making the task profitable. ps. If you have an assembly task that we can do for you asynchronically - leave a comment below. Learning 1. It's possible! This task which is usually done by the human arm with 5 fingers can be done with a two-finger gripper with the addition of a couple of simple tooling. The task was not simplified. We peeled off thin films from stickers, unpacked paper boxes, moved PCB boards full of components, etc. And no unsolvable problems have been encountered yet. Challenges: 1) The paper box shifted during the opening Solved with the plastic walls that you can lean against 2) Heat pad, stuck to the gripper instead of heat sync. Can be solved by gripper with a pump, but this time solved with the patience of the operator 3) The film on the pad is very thin. Turned out that sub-millimeter arm precision is enough to peel it off with just a regular gripper. 4) The working area has not enough space. You'll only know this by doing real tasks in bulk. This could be solved by an extra pair of long arms, but in this case, solved with the patience of the operator. I think that in the end, we will have 5-10 types of universal tooling and 5-10 types of grippers to solve almost all the problems in such assembly tasks. Learning 2. It's slow. It took 5 times more time, than doing it with human hands. But the good news is there's a lot of room for improvement. We now have specific “time for task” metrics, which we will decrease with iterations. The main reasons for slowness: 1) To rotate the gripper to a steep angle you are forced to control one robot arm with two hands instead of using both arms. We can fix this by just making more room for rotations. 2) Grabbing PCB board with two arms is hard. A slight difference in rotation can break the board, and it's hard to control these angles visually. To solve this, the best way is to use force feedback so you can feel the pressure applied to the item. 3) Accuracy and steadiness is still can be improved We will try a metal version and double the motors to do this. 4) It is physically difficult for the human hands to move with such precision To solve this, we will add a pad for the hands like in surgical robots Learning 3. It's a good business model The "Factory in the cloud" is a good business model for this stage. You send us parts and we send back assembled modules. Currently, it's more convenient than sending a robot to your place, as we can iterate/fix the robot quickly and utilize it 100% of the time. When we polish the set-up over time - we can send robots to your place. So if we can assemble something for you in the USA with Chinese prices by using modern automation - leave a comment below.
0:48

Milestone! We (robotic arms for gadgets assembly) finished the first commercial order, which brought the first revenue. Here are some learnings from this: The customer was a smart toy manufacturer. The task was to add a heatsink to Raspberry Pi. We received parts from them and returned the assembled modules back. Currently, it's done by teleoperation. Later it will be done by a remote employee via the Internet. Then it will be automated action by action, reducing the operator's time on this and making the task profitable. ps. If you have an assembly task that we can do for you asynchronically - leave a comment below. Learning 1. It's possible! This task which is usually done by the human arm with 5 fingers can be done with a two-finger gripper with the addition of a couple of simple tooling. The task was not simplified. We peeled off thin films from stickers, unpacked paper boxes, moved PCB boards full of components, etc. And no unsolvable problems have been encountered yet. Challenges: 1) The paper box shifted during the opening Solved with the plastic walls that you can lean against 2) Heat pad, stuck to the gripper instead of heat sync. Can be solved by gripper with a pump, but this time solved with the patience of the operator 3) The film on the pad is very thin. Turned out that sub-millimeter arm precision is enough to peel it off with just a regular gripper. 4) The working area has not enough space. You'll only know this by doing real tasks in bulk. This could be solved by an extra pair of long arms, but in this case, solved with the patience of the operator. I think that in the end, we will have 5-10 types of universal tooling and 5-10 types of grippers to solve almost all the problems in such assembly tasks. Learning 2. It's slow. It took 5 times more time, than doing it with human hands. But the good news is there's a lot of room for improvement. We now have specific “time for task” metrics, which we will decrease with iterations. The main reasons for slowness: 1) To rotate the gripper to a steep angle you are forced to control one robot arm with two hands instead of using both arms. We can fix this by just making more room for rotations. 2) Grabbing PCB board with two arms is hard. A slight difference in rotation can break the board, and it's hard to control these angles visually. To solve this, the best way is to use force feedback so you can feel the pressure applied to the item. 3) Accuracy and steadiness is still can be improved We will try a metal version and double the motors to do this. 4) It is physically difficult for the human hands to move with such precision To solve this, we will add a pad for the hands like in surgical robots Learning 3. It's a good business model The "Factory in the cloud" is a good business model for this stage. You send us parts and we send back assembled modules. Currently, it's more convenient than sending a robot to your place, as we can iterate/fix the robot quickly and utilize it 100% of the time. When we polish the set-up over time - we can send robots to your place. So if we can assemble something for you in the USA with Chinese prices by using modern automation - leave a comment below.

Igor Kulakov

37,266 次观看 • 1 年前

Holy…S😳 Xiaomi's New CyberOne is so human-like Although this update features a bionic hand, I was immediately drawn to it. Let's look at the changes in the hand： It can handle industrial precision tasks like turning screws, plus delicate operations such as pinching feathers and throwing balloons. Behind the performance: >Volume cut by 60%:now almost identical in size/shape to a real human hand >Big leap in degrees of freedom (+50% total, +83% active:22-27DOF) >Full-palm tactile sensors over 8200 mm² for precise grip even without vision >150,000+ grip cycles durability (61-hour test) And a major innovation:Smart bionic sweat gland cooling: evaporates water for ~10W active heat dissipation Using tactile gloves to capture real human data, they’re training smoother, human-like grasps with imitation and reinforcement learning. Elon has said that humanoid hands and true AI are the most difficult aspects of building humanoid robots. It seems that Xiaomi is also getting close.
0:41

Holy…S😳 Xiaomi's New CyberOne is so human-like Although this update features a bionic hand, I was immediately drawn to it. Let's look at the changes in the hand： It can handle industrial precision tasks like turning screws, plus delicate operations such as pinching feathers and throwing balloons. Behind the performance: >Volume cut by 60%:now almost identical in size/shape to a real human hand >Big leap in degrees of freedom (+50% total, +83% active:22-27DOF) >Full-palm tactile sensors over 8200 mm² for precise grip even without vision >150,000+ grip cycles durability (61-hour test) And a major innovation:Smart bionic sweat gland cooling: evaporates water for ~10W active heat dissipation Using tactile gloves to capture real human data, they’re training smoother, human-like grasps with imitation and reinforcement learning. Elon has said that humanoid hands and true AI are the most difficult aspects of building humanoid robots. It seems that Xiaomi is also getting close.

CyberRobo

111,430 次观看 • 5 个月前

A new dexterous hand is here. DexcelRobotics, a startup founded by a former core member of Tencent Robotics X, has launched its first product, the Apex Hand. The company claims it's the first in the industry capable of operating a cell phone with a single hand. The Apex Hand is a well-rounded performer, with a focus on real-world application. Here are its key specs: ► Degrees of Freedom: 21, which the founder notes is enough to replicate most human hand functions. ► Strength: A single-finger force of ~2.5kg and a vertical lifting capacity of ~30kg. ► Speed & Precision: A response time near human-level, with positioning accuracy of ≤0.1mm. ► Robustness: Can withstand unexpected impacts and maintain stability. ► Tactile: Features self-developed flexible electronic skin with a sub-millisecond communication delay and >1000Hz refresh rate. The founder explains that while academic research often focuses on single-point breakthroughs, a commercial product needs hardware stability, data handling, and model capabilities. The company’s full-stack development experience and modular design are key to bringing this technology to a broader market, starting with semi-structured industrial environments before reaching homes.
2:58

A new dexterous hand is here. DexcelRobotics, a startup founded by a former core member of Tencent Robotics X, has launched its first product, the Apex Hand. The company claims it's the first in the industry capable of operating a cell phone with a single hand. The Apex Hand is a well-rounded performer, with a focus on real-world application. Here are its key specs: ► Degrees of Freedom: 21, which the founder notes is enough to replicate most human hand functions. ► Strength: A single-finger force of ~2.5kg and a vertical lifting capacity of ~30kg. ► Speed & Precision: A response time near human-level, with positioning accuracy of ≤0.1mm. ► Robustness: Can withstand unexpected impacts and maintain stability. ► Tactile: Features self-developed flexible electronic skin with a sub-millisecond communication delay and >1000Hz refresh rate. The founder explains that while academic research often focuses on single-point breakthroughs, a commercial product needs hardware stability, data handling, and model capabilities. The company’s full-stack development experience and modular design are key to bringing this technology to a broader market, starting with semi-structured industrial environments before reaching homes.

RoboHub🤖

125,561 次观看 • 11 个月前

I’m thrilled to announce that we just released GraspGen, a multi-year project we have been cooking at NVIDIA Robotics 🚀 GraspGen: A Diffusion-Based Framework for 6-DOF Grasping Grasping is a foundational challenge in robotics 🤖 — whether for industrial picking or general-purpose humanoids. VLA + real data collection is all the rage now but is expensive and scales poorly for this task. For every new gripper and/or scene, you’ll have to recollect the dataset in this paradigm for the best perf. 💡Key Idea: Since grasping is such a well-defined task in simulation - why can’t we just scale synthetic data generation and train a generative model for grasping? By embracing modularity and standardized grasp formats, we can make this a turnkey technology that works zero-shot for multiple settings. GraspGen is a modular framework for diffusion-based 6-DOF grasp generation that scales across embodiment types, observability conditions, clutter, task complexity. Key Features: ✅ Multi-embodiment support: suction, parallel-jaw, and multi-fingered grippers ✅ Generalization to partial + complete 3D point clouds ✅ Generalization to single-objects + cluttered scenes ✅ Modular design uses other robotics modules and foundation models (SAM2, cuRobo, FoundationStereo, FoundationPose). This allows GraspGen to focus on only one thing - grasp generation ✅ Training recipe: grasp discriminator is trained with On-Generator data from the diffusion model - so that it learns to correct the mistakes (if any) of the diffusion generator ✅ Real-time performance (~20 Hz) before any GPU acceleration; low memory footprint 📊 Results: • SOTA on the FetchBench [Han et al. CoRL 2024] benchmark • Zero-shot sim-to-real transfer on unknown objects and cluttered scenes • Dataset of 53M simulated grasps across 8K objects from Objaverse 📄 arXiv: 🌐 Website: 💻 Code: A huge thank you to everyone involved in this journey — excited to see what the community builds on top of it! Joint work with Clemens Eppner , Balakumar Sundaralingam , Yu-Wei, Jun Yamada Wentao Yuan and other collaborators #robotics #diffusionmodels #physicalAI #simtoreal
0:39

I’m thrilled to announce that we just released GraspGen, a multi-year project we have been cooking at NVIDIA Robotics 🚀 GraspGen: A Diffusion-Based Framework for 6-DOF Grasping Grasping is a foundational challenge in robotics 🤖 — whether for industrial picking or general-purpose humanoids. VLA + real data collection is all the rage now but is expensive and scales poorly for this task. For every new gripper and/or scene, you’ll have to recollect the dataset in this paradigm for the best perf. 💡Key Idea: Since grasping is such a well-defined task in simulation - why can’t we just scale synthetic data generation and train a generative model for grasping? By embracing modularity and standardized grasp formats, we can make this a turnkey technology that works zero-shot for multiple settings. GraspGen is a modular framework for diffusion-based 6-DOF grasp generation that scales across embodiment types, observability conditions, clutter, task complexity. Key Features: ✅ Multi-embodiment support: suction, parallel-jaw, and multi-fingered grippers ✅ Generalization to partial + complete 3D point clouds ✅ Generalization to single-objects + cluttered scenes ✅ Modular design uses other robotics modules and foundation models (SAM2, cuRobo, FoundationStereo, FoundationPose). This allows GraspGen to focus on only one thing - grasp generation ✅ Training recipe: grasp discriminator is trained with On-Generator data from the diffusion model - so that it learns to correct the mistakes (if any) of the diffusion generator ✅ Real-time performance (~20 Hz) before any GPU acceleration; low memory footprint 📊 Results: • SOTA on the FetchBench [Han et al. CoRL 2024] benchmark • Zero-shot sim-to-real transfer on unknown objects and cluttered scenes • Dataset of 53M simulated grasps across 8K objects from Objaverse 📄 arXiv: 🌐 Website: 💻 Code: A huge thank you to everyone involved in this journey — excited to see what the community builds on top of it! Joint work with Clemens Eppner , Balakumar Sundaralingam , Yu-Wei, Jun Yamada Wentao Yuan and other collaborators #robotics #diffusionmodels #physicalAI #simtoreal

Adithya Murali

24,106 次观看 • 1 年前

There is something profoundly powerful about watching a parent step back and let a child struggle just enough to learn. In a world where it’s often easier to just "do it for them" to save time, seeing a father provide the tools and the safety net—but not the easy way out—is the ultimate blueprint for building confidence. By guiding her through the mechanics of taking off those training wheels, he isn't just teaching her how to ride a bike; he’s teaching her that she is capable of solving her own problems. You can see the shift in her energy the moment she realizes the wrench is actually moving. That isn't just a physical win; it’s the birth of a "can-do" mindset. When a child is allowed to apply themselves and see a tangible result from their own effort, they stop looking for someone else to fix things and start looking for a way to do it themselves. This is how self-reliance is built. Not through lectures, but through grease on the hands, a little bit of patience, and a parent who stays close enough to catch them but far enough to let them lead. We need to see more of this intentional, hands-on guidance. It’s the kind of foundation that stays with a person for a lifetime.
2:44

There is something profoundly powerful about watching a parent step back and let a child struggle just enough to learn. In a world where it’s often easier to just "do it for them" to save time, seeing a father provide the tools and the safety net—but not the easy way out—is the ultimate blueprint for building confidence. By guiding her through the mechanics of taking off those training wheels, he isn't just teaching her how to ride a bike; he’s teaching her that she is capable of solving her own problems. You can see the shift in her energy the moment she realizes the wrench is actually moving. That isn't just a physical win; it’s the birth of a "can-do" mindset. When a child is allowed to apply themselves and see a tangible result from their own effort, they stop looking for someone else to fix things and start looking for a way to do it themselves. This is how self-reliance is built. Not through lectures, but through grease on the hands, a little bit of patience, and a parent who stays close enough to catch them but far enough to let them lead. We need to see more of this intentional, hands-on guidance. It’s the kind of foundation that stays with a person for a lifetime.

PeachProof

16,764 次观看 • 3 个月前