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From "The Force" to Real Force: Gesture control for this tentacle #robot #SquidBot! 🤖🦑 Camera + #ComputerVision = Hand tracking. #3D Tentacle (flexible #TPU) + Fishing line = Realistic movement. 💥🖖 What object should I ask it to grab next? 👇 #Robotics #CES2026 #CES26 #AI

Fabrizio Bustamante Escudero

42,875 subscribers

17,042 次观看 • 7 个月前 •via X (Twitter)

科学技术 #robot #SquidBot #ComputerVision #3D #TPU #Robotics #CES2026 #CES26 #AI

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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

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

Today I realized something absolutely with BitRobot Network You dont need to own a robot… to actually control one I was just sitting at my laptop and somehow I was moving a real robotic arm in real time At first I thought it would feel like a video game It didnt Theres a completely different feeling when you know this is happening in the real world not just on a screen Every movement matters. Every action generates data And then it hit me… This is how AI is learning to interact with the real world Not from code alone but from people like us Platforms like BitRobot are opening this to everyone No setup No cost No experience Just curiosity and the internet Someone sitting at home can now help train the next generation of robotics. The future isnt coming its happening right now. If you could control a robot right now what would you try first? 🤖👇

Today I realized something absolutely with BitRobot Network You dont need to own a robot… to actually control one I was just sitting at my laptop and somehow I was moving a real robotic arm in real time At first I thought it would feel like a video game It didnt Theres a completely different feeling when you know this is happening in the real world not just on a screen Every movement matters. Every action generates data And then it hit me… This is how AI is learning to interact with the real world Not from code alone but from people like us Platforms like BitRobot are opening this to everyone No setup No cost No experience Just curiosity and the internet Someone sitting at home can now help train the next generation of robotics. The future isnt coming its happening right now. If you could control a robot right now what would you try first? 🤖👇

RAJU

40,141 次观看 • 4 个月前

🤖What if a robot could perform a new task just from a natural language command, with zero demonstrations? Our new work, NovaFlow, makes it possible! We use pre-trained video generative model to create a video of the task, then translate it into a plan for real-world robot execution. 1/6 #Robotics #AI #ZeroShot #Manipulation

🤖What if a robot could perform a new task just from a natural language command, with zero demonstrations? Our new work, NovaFlow, makes it possible! We use pre-trained video generative model to create a video of the task, then translate it into a plan for real-world robot execution. 1/6 #Robotics #AI #ZeroShot #Manipulation

Hongyu Li

105,753 次观看 • 10 个月前

A 40-year-old patent has finally been brought to life. That's the Y-zipper. A 3D-printed three-sided fastener that transitions any object from flexible to rigid and back again. The robotics application is the one that caught my attention. A quadruped robot that adjusts its leg stiffness depending on terrain, switching between rigid and flexible in real time without additional motors or complex mechanical systems. But this goes way beyond robotics. A wrist cast that loosens during the day and stiffens at night. A tent that pops into shape in 90 seconds instead of six minutes. The idea sat in a patent filing for four decades. It took 3D printing to finally make it real. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

A 40-year-old patent has finally been brought to life. That's the Y-zipper. A 3D-printed three-sided fastener that transitions any object from flexible to rigid and back again. The robotics application is the one that caught my attention. A quadruped robot that adjusts its leg stiffness depending on terrain, switching between rigid and flexible in real time without additional motors or complex mechanical systems. But this goes way beyond robotics. A wrist cast that loosens during the day and stiffens at night. A tent that pops into shape in 90 seconds instead of six minutes. The idea sat in a patent filing for four decades. It took 3D printing to finally make it real. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

1,805,759 次观看 • 3 个月前

A GUY PUT GPT-LEVEL AI INSIDE A 3D PRINTED ROBOT AND RELEASED THE ENTIRE THING FOR FREE Most people look at a 3D printer and see plastic toys This guy printed a robot with expressive eyes, object tracking, image recognition, support for ChatGPT, Qwen and offline models, then uploaded the STL files, code and hardware designs for anyone to build themselves The interesting part isn't the robot It's how fast the gap between "I have an idea" and "I built a working product" is disappearing A few years ago this required a team of engineers Now one person with a printer, open-source software and enough curiosity can build something that looks like it came from a robotics startup We're getting very close to a world where manufacturing, AI and robotics stop being separate industries and start becoming the same thing

A GUY PUT GPT-LEVEL AI INSIDE A 3D PRINTED ROBOT AND RELEASED THE ENTIRE THING FOR FREE Most people look at a 3D printer and see plastic toys This guy printed a robot with expressive eyes, object tracking, image recognition, support for ChatGPT, Qwen and offline models, then uploaded the STL files, code and hardware designs for anyone to build themselves The interesting part isn't the robot It's how fast the gap between "I have an idea" and "I built a working product" is disappearing A few years ago this required a team of engineers Now one person with a printer, open-source software and enough curiosity can build something that looks like it came from a robotics startup We're getting very close to a world where manufacturing, AI and robotics stop being separate industries and start becoming the same thing

lagerskoy

57,099 次观看 • 2 个月前

Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:

Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

32,025 次观看 • 7 个月前

Full-body joint tracking + smart gloves capture every nuance of human motion, which is retargeted in real-time to a humanoid robot (here, Tesla Optimus) for smooth teleoperation. This is the power of inertial motion capture. More than control, mocap is the gold standard for collecting high-fidelity humanoid training data. Advanced AI algorithms transform raw accelerometer streams into precise 3D joint poses, turning human movement into lifelike robot action.

Full-body joint tracking + smart gloves capture every nuance of human motion, which is retargeted in real-time to a humanoid robot (here, Tesla Optimus) for smooth teleoperation. This is the power of inertial motion capture. More than control, mocap is the gold standard for collecting high-fidelity humanoid training data. Advanced AI algorithms transform raw accelerometer streams into precise 3D joint poses, turning human movement into lifelike robot action.

Reborn

95,507 次观看 • 1 年前

Feeling what a robot feels is becoming real! 🫰 Fluid Reality just ran its first full end-to-end touch teleoperation system, and the demo shows why high-resolution haptics might be the missing piece in robot control. A 22 mm fingertip display with 32 independently actuated “bubbles” was mapped directly to tactile sensors on a robot hand. 🫧 Three fingers streamed real-time touch data robot → human, giving the operator actual contact feedback instead of waving their hands blind, like most teleop systems today. It’s still early, but this is a real step toward dexterous ops and high-quality data for training Physical AI systems. Companies involved: Samsung Electronics, Alt-Bionics, Inc. (the robot hand), MANUS™ (hand tracking), and Sensible Robotics (fingertip sensors) Touch isn’t a nice-to-have in robotics. It’s the difference between manipulation that works in the real world P.S. Guess how many touch receptors we humans, have in our hand! :) ~~ ♻ Join the weekly robotics newsletter, and never miss any news →

Feeling what a robot feels is becoming real! 🫰 Fluid Reality just ran its first full end-to-end touch teleoperation system, and the demo shows why high-resolution haptics might be the missing piece in robot control. A 22 mm fingertip display with 32 independently actuated “bubbles” was mapped directly to tactile sensors on a robot hand. 🫧 Three fingers streamed real-time touch data robot → human, giving the operator actual contact feedback instead of waving their hands blind, like most teleop systems today. It’s still early, but this is a real step toward dexterous ops and high-quality data for training Physical AI systems. Companies involved: Samsung Electronics, Alt-Bionics, Inc. (the robot hand), MANUS™ (hand tracking), and Sensible Robotics (fingertip sensors) Touch isn’t a nice-to-have in robotics. It’s the difference between manipulation that works in the real world P.S. Guess how many touch receptors we humans, have in our hand! :) ~~ ♻ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

46,927 次观看 • 8 个月前

I ran a quick test of Gemini Robotics ER 1.5 controlling my LeKiwi using the MCP I developed before. No extra code required - just specified a new model. Unfortunately, it doesn't work well, even though I had expected it to be the best, as it is the first major VLM model trained specifically for robotics applications. The video below is a relatively good attempt. It does understand what is going on and generates a reasonable action plan, but it is not doing well with the low-level control. I guess it was not trained for this and was used together with the VLA, so it is trained to generate more high-level commands like "grab this object and put it there" rather than doing low-level control specifying every robot movement step by step. Did anybody try to use it in a similar setup? What is your experience?

I ran a quick test of Gemini Robotics ER 1.5 controlling my LeKiwi using the MCP I developed before. No extra code required - just specified a new model. Unfortunately, it doesn't work well, even though I had expected it to be the best, as it is the first major VLM model trained specifically for robotics applications. The video below is a relatively good attempt. It does understand what is going on and generates a reasonable action plan, but it is not doing well with the low-level control. I guess it was not trained for this and was used together with the VLA, so it is trained to generate more high-level commands like "grab this object and put it there" rather than doing low-level control specifying every robot movement step by step. Did anybody try to use it in a similar setup? What is your experience?

Ilia

15,640 次观看 • 11 个月前

A humanoid robot playing a Yamaha drum kit is one of the cleanest ways to test rhythm control in public. Drumming exposes timing, wrist motion, impact force, arm recovery, and coordination between both hands. Even if the routine is pre-programmed or assisted, the useful part is visible: the robot has to strike, reset, and stay in rhythm without turning into random arm movement. Robot musicians may look funny today. They are quietly testing the same control problems needed for household robots, factory arms, and physical AI systems that work around humans. Would you call this entertainment, robotics research, or the early market for robot performers?

A humanoid robot playing a Yamaha drum kit is one of the cleanest ways to test rhythm control in public. Drumming exposes timing, wrist motion, impact force, arm recovery, and coordination between both hands. Even if the routine is pre-programmed or assisted, the useful part is visible: the robot has to strike, reset, and stay in rhythm without turning into random arm movement. Robot musicians may look funny today. They are quietly testing the same control problems needed for household robots, factory arms, and physical AI systems that work around humans. Would you call this entertainment, robotics research, or the early market for robot performers?

TechniaHQ | humanoid robots

208,048 次观看 • 3 个月前

This is so cool! 🤯🤯 I just tested the new Avatar IV lip-syncing model by HeyGen. It now includes hand gesture control, and the results are impressive! This could be a strong solution for creating consistent AI characters with multiple camera angles. I’m excited to use it in my upcoming short films!

Hashem Al-Ghaili

26,439 次观看 • 1 年前

Open-source dexterous hands with fingertip sensors! 🪬 ORCA Dexterity just released three dexterous hand models. Their mission: is to democratize robotic hand dexterity. They're sharing progress on exceptional, low-cost hardware and a software layer from low-level control to robotic hand learning. This is the open-source approach to dexterous manipulation. It has 83 taxels per finger with 0.1 N force detection which pretty is impressive for an open-source design. Tactile sensing is critical for dexterous manipulation, knowing contact forces enables gentle grasping, slip detection, and force-controlled assembly. Also 700g weight for the lite version makes it practical for mounting on robot arms without exceeding payload limits. Lower weight means faster movements and lower torque requirements. Open hardware accelerates robotics by letting researchers and builders modify designs for their specific needs without starting from scratch! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Open-source dexterous hands with fingertip sensors! 🪬 ORCA Dexterity just released three dexterous hand models. Their mission: is to democratize robotic hand dexterity. They're sharing progress on exceptional, low-cost hardware and a software layer from low-level control to robotic hand learning. This is the open-source approach to dexterous manipulation. It has 83 taxels per finger with 0.1 N force detection which pretty is impressive for an open-source design. Tactile sensing is critical for dexterous manipulation, knowing contact forces enables gentle grasping, slip detection, and force-controlled assembly. Also 700g weight for the lite version makes it practical for mounting on robot arms without exceeding payload limits. Lower weight means faster movements and lower torque requirements. Open hardware accelerates robotics by letting researchers and builders modify designs for their specific needs without starting from scratch! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

38,094 次观看 • 5 个月前

More beautiful robot hands. This one from Sharpa The hand uses what they call a Dynamic Tactile Array, meaning vision and touch work together and their SaTA (Spatially-anchored Tactile Awareness) algorithm. Each fingertip includes a mini camera and 1,000+ tactile pixels, hitting 0.005N pressure sensitivity. With 22 DOF and 6D force sensing, it can do everything from eggs to tools.

More beautiful robot hands. This one from Sharpa The hand uses what they call a Dynamic Tactile Array, meaning vision and touch work together and their SaTA (Spatially-anchored Tactile Awareness) algorithm. Each fingertip includes a mini camera and 1,000+ tactile pixels, hitting 0.005N pressure sensitivity. With 22 DOF and 6D force sensing, it can do everything from eggs to tools.

Rohan Paul

41,775 次观看 • 8 个月前

Better data for robot hands! 🤲🏼 Embodied reasoning is the bottleneck now. It’s the data we are missing. Specifically, understanding what the robot's hands are doing throughout a task. Perceptron AI new Egocentric offering tracks both hands through an entire video instead of guessing from sampled frames. That matters because a lot happens between frames. Hand positions shift. Contacts change. The robot adjusts grip. You miss all that if you're sampling. 0.280 semantic end-to-end F1 score on WGO-Bench vs 0.158 from the best pipelines built on Gemini Robotics-ER 1.6. Training a robot to manipulate objects requires understanding every moment of hand-object interaction. If your training data has gaps: missing frames, missed contacts, unclear hand positions, the policy learns from incomplete information. You end up with robots that work in demos but fail in real tasks. Continuous hand tracking gives you the full picture. The policy learns from cleaner, more complete data. That compounds into better real-world performance. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Better data for robot hands! 🤲🏼 Embodied reasoning is the bottleneck now. It’s the data we are missing. Specifically, understanding what the robot's hands are doing throughout a task. Perceptron AI new Egocentric offering tracks both hands through an entire video instead of guessing from sampled frames. That matters because a lot happens between frames. Hand positions shift. Contacts change. The robot adjusts grip. You miss all that if you're sampling. 0.280 semantic end-to-end F1 score on WGO-Bench vs 0.158 from the best pipelines built on Gemini Robotics-ER 1.6. Training a robot to manipulate objects requires understanding every moment of hand-object interaction. If your training data has gaps: missing frames, missed contacts, unclear hand positions, the policy learns from incomplete information. You end up with robots that work in demos but fail in real tasks. Continuous hand tracking gives you the full picture. The policy learns from cleaner, more complete data. That compounds into better real-world performance. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

18,348 次观看 • 1 个月前

We might be solving the wrong problem in robotics. That’s what this makes clear. UMI → Universal Manipulation Interface A simple $400 gripper that lets you teach robots by demonstration. You hold it like a tool. Show the task. The robot learns. No teleoperation. No expensive hardware. No robot-specific data. Stanford open-sourced everything → hardware, code, datasets. What stands out to me is the bottleneck. Not algorithms. Data. Teleoperation → ~35 demos/hour UMI → ~111 demos/hour And the data transfers across robots → UR5, Franka, others. The design is surprisingly practical: → GoPro fisheye lens (155° FOV) + mirrors for depth → SLAM + IMU for precise 6DoF tracking → latency matching for dynamic tasks → diffusion policies for multimodal actions Then it scales. Cheng Chi takes this further with Sunday Robotics (with Tony Zhao). A $200 glove → deployed in 500+ homes → ~10 million real-world interactions. Not lab data. Real human behavior. Their robot learns dishes, laundry, espresso → with zero robot-specific data. This is where the shift becomes obvious. From training robots in controlled environments → to learning directly from humans at scale So here’s the real question: Will robotics be unlocked by better models… or by unlocking data? #ArtificialIntelligence #Robotics #AI #Innovation #FutureOfWork

We might be solving the wrong problem in robotics. That’s what this makes clear. UMI → Universal Manipulation Interface A simple $400 gripper that lets you teach robots by demonstration. You hold it like a tool. Show the task. The robot learns. No teleoperation. No expensive hardware. No robot-specific data. Stanford open-sourced everything → hardware, code, datasets. What stands out to me is the bottleneck. Not algorithms. Data. Teleoperation → ~35 demos/hour UMI → ~111 demos/hour And the data transfers across robots → UR5, Franka, others. The design is surprisingly practical: → GoPro fisheye lens (155° FOV) + mirrors for depth → SLAM + IMU for precise 6DoF tracking → latency matching for dynamic tasks → diffusion policies for multimodal actions Then it scales. Cheng Chi takes this further with Sunday Robotics (with Tony Zhao). A $200 glove → deployed in 500+ homes → ~10 million real-world interactions. Not lab data. Real human behavior. Their robot learns dishes, laundry, espresso → with zero robot-specific data. This is where the shift becomes obvious. From training robots in controlled environments → to learning directly from humans at scale So here’s the real question: Will robotics be unlocked by better models… or by unlocking data? #ArtificialIntelligence #Robotics #AI #Innovation #FutureOfWork

Pascal Bornet

186,670 次观看 • 4 个月前

AI can't replace a 3D artist yet. I gave a loop of AI agents two days to fix my animations. This is what I got back. Day 15 + 16 of building my own GTA 6 with a loop of AI agents. Today's progress: - Aiming works now, you can line up a shot properly. - Camera movement is in, it follows you the way it should. - Walking turns look right, no more sliding around corners. We have spent two whole days letting the loop work on the animations and got basically no improvement. The ones we're ending up with and the free ones off Mixamo both look cheap. So we're probably just going to buy a premium animation pack off Epic and hand it to Claude. Good news is aiming and the camera feel real now. Making the character move like it isn't a cheap asset is the next wall.

AI can't replace a 3D artist yet. I gave a loop of AI agents two days to fix my animations. This is what I got back. Day 15 + 16 of building my own GTA 6 with a loop of AI agents. Today's progress: - Aiming works now, you can line up a shot properly. - Camera movement is in, it follows you the way it should. - Walking turns look right, no more sliding around corners. We have spent two whole days letting the loop work on the animations and got basically no improvement. The ones we're ending up with and the free ones off Mixamo both look cheap. So we're probably just going to buy a premium animation pack off Epic and hand it to Claude. Good news is aiming and the camera feel real now. Making the character move like it isn't a cheap asset is the next wall.

Ziwen

320,498 次观看 • 2 个月前

🤖⚙️AGIBOT G2: The Next Generation of Industrial-Grade Interactive Embodied Operation Robots Sub-millimeter precision, human-like motion, 24/7 non-stop operation — AGIBOT G2 is the industrial-grade interactive embodied operation robot your factory needs! Dual arms with ≤0.5N force control for submillimeter accuracy, 26-DOF full-body movement, and NVIDIA-powered ultra computing. From high-precision assembly to logistics sorting, it’s redefining human-robot collaboration across every industry. The future of industrial automation is here. #AGIBOT #HumanoidNo1 #HumanoidShipmentsNo1 #AGIBOTG2 #IndustrialRobotics #Automation #SmartManufacturing

🤖⚙️AGIBOT G2: The Next Generation of Industrial-Grade Interactive Embodied Operation Robots Sub-millimeter precision, human-like motion, 24/7 non-stop operation — AGIBOT G2 is the industrial-grade interactive embodied operation robot your factory needs! Dual arms with ≤0.5N force control for submillimeter accuracy, 26-DOF full-body movement, and NVIDIA-powered ultra computing. From high-precision assembly to logistics sorting, it’s redefining human-robot collaboration across every industry. The future of industrial automation is here. #AGIBOT #HumanoidNo1 #HumanoidShipmentsNo1 #AGIBOTG2 #IndustrialRobotics #Automation #SmartManufacturing

AGIBOT

21,290 次观看 • 6 个月前

This is the Scorpion Hexapod, a six-legged robotic scorpion created at Ghent University UGent Campus Kortrijk in Belgium. It was built by students Stephan Flamand Robbe Terryn and Pieterjan Deconinck as part of an Embedded Prototyping / Mechatronics Design project. What it is • A biomimetic robot inspired by the body and movement of a real scorpion • A hexapod, meaning it walks on six legs • A university prototype, not a commercial robot • Built to test animal-inspired movement, sensors and interactive behavior • Designed more for robotics research and education than real-world work Main hardware • 6 walking legs for crawling movement • 2 front claws for the scorpion look • Moving tail with a stinger-style mechanism • Sensors in the body, legs and claws • Arduino-based electronics for control • Front camera and proximity sensing • Battery pack for mobile operation • 3D-printed modules for legs and tail • Laser-cut ABS body parts • Thermoformed shell for the white outer body What it can do • Walk across the floor using its six legs • Move its tail like a real scorpion • React when a person gets close • Detect when someone covers its front sensors • Strike with its tail in the demo • Leave a red mark using a marker pen attached to the stinger • Operate through remote control • Perform some simple autonomous reactions Why it was created • To explore bio-inspired robotics • To show how digital fabrication can produce complex moving robots • To combine 3D printing, laser cutting, Arduino electronics and sensors • To teach students how to design a complete mechatronic system • To improve on an older robotic ant project that had weak autonomy, short battery life and motor problems • To create an interactive robot that reacts to humans in a visible way Important note • It was not built for combat • It was not made for industrial deployment • It is not a military robot • It is an educational robotics prototype made to demonstrate movement, sensing and interaction

This is the Scorpion Hexapod, a six-legged robotic scorpion created at Ghent University UGent Campus Kortrijk in Belgium. It was built by students Stephan Flamand Robbe Terryn and Pieterjan Deconinck as part of an Embedded Prototyping / Mechatronics Design project. What it is • A biomimetic robot inspired by the body and movement of a real scorpion • A hexapod, meaning it walks on six legs • A university prototype, not a commercial robot • Built to test animal-inspired movement, sensors and interactive behavior • Designed more for robotics research and education than real-world work Main hardware • 6 walking legs for crawling movement • 2 front claws for the scorpion look • Moving tail with a stinger-style mechanism • Sensors in the body, legs and claws • Arduino-based electronics for control • Front camera and proximity sensing • Battery pack for mobile operation • 3D-printed modules for legs and tail • Laser-cut ABS body parts • Thermoformed shell for the white outer body What it can do • Walk across the floor using its six legs • Move its tail like a real scorpion • React when a person gets close • Detect when someone covers its front sensors • Strike with its tail in the demo • Leave a red mark using a marker pen attached to the stinger • Operate through remote control • Perform some simple autonomous reactions Why it was created • To explore bio-inspired robotics • To show how digital fabrication can produce complex moving robots • To combine 3D printing, laser cutting, Arduino electronics and sensors • To teach students how to design a complete mechatronic system • To improve on an older robotic ant project that had weak autonomy, short battery life and motor problems • To create an interactive robot that reacts to humans in a visible way Important note • It was not built for combat • It was not made for industrial deployment • It is not a military robot • It is an educational robotics prototype made to demonstrate movement, sensing and interaction

Techniahqrobot | humanoid robots

28,154 次观看 • 1 个月前

15–30 minutes of real-world robot data. That's now enough to go from sim-to-real failure to working robot. Let’s see… You train a robot in simulation. You deploy it in the real world. It fails. The physics don't match. So you try to fine-tune it with real data, but… you never have enough real data, and the fine-tuning breaks everything the simulation taught it. SimDist fixes this with one key decision: don't transfer the policy. Transfer the world model. Keep the reward and value knowledge from simulation frozen. Only update the part that's actually wrong, how the robot predicts physics. Now the robot doesn't have to relearn the entire task in the real world. It already knows what success looks like. It just needs to correct its understanding of how the real world moves. The part that makes this work: they also trained on failures and recoveries; not just perfect demonstrations. Without that, the planner finds the gaps and exploits them. With it, the robot can tell a good future from a bad one. That's all it needs. Results on peg insertion, table leg assembly, locomotion on slippery and uneven surfaces. Tasks that require precision, force, and quick reaction. Thanks for sharing, Tyler Westenbroek ([Tyler Westenbroek]. Interactive visualization + paper: ——- Weekly robotics and AI insights. Subscribe free:

15–30 minutes of real-world robot data. That's now enough to go from sim-to-real failure to working robot. Let’s see… You train a robot in simulation. You deploy it in the real world. It fails. The physics don't match. So you try to fine-tune it with real data, but… you never have enough real data, and the fine-tuning breaks everything the simulation taught it. SimDist fixes this with one key decision: don't transfer the policy. Transfer the world model. Keep the reward and value knowledge from simulation frozen. Only update the part that's actually wrong, how the robot predicts physics. Now the robot doesn't have to relearn the entire task in the real world. It already knows what success looks like. It just needs to correct its understanding of how the real world moves. The part that makes this work: they also trained on failures and recoveries; not just perfect demonstrations. Without that, the planner finds the gaps and exploits them. With it, the robot can tell a good future from a bad one. That's all it needs. Results on peg insertion, table leg assembly, locomotion on slippery and uneven surfaces. Tasks that require precision, force, and quick reaction. Thanks for sharing, Tyler Westenbroek ([Tyler Westenbroek]. Interactive visualization + paper: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

14,222 次观看 • 3 个月前